Package: alookr
Check: examples
New result: ERROR
  Running examples in ‘alookr-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: run_models
  > ### Title: Fit binary classification model
  > ### Aliases: run_models
  > 
  > ### ** Examples
  > 
  > library(dplyr)
  
  Attaching package: ‘dplyr’
  
  The following object is masked from ‘package:randomForest’:
  
      combine
  
  The following objects are masked from ‘package:stats’:
  
      filter, lag
  
  The following objects are masked from ‘package:base’:
  
      intersect, setdiff, setequal, union
  
  > 
  > # Divide the train data set and the test data set.
  > sb <- rpart::kyphosis %>%
  +   split_by(Kyphosis)
  > 
  > # Extract the train data set from original data set.
  > train <- sb %>%
  +   extract_set(set = "train")
  > 
  > # Extract the test data set from original data set.
  > test <- sb %>%
  +   extract_set(set = "test")
  > 
  > # Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique).
  > train <- sb %>%
  +   sampling_target(seed = 1234L, method = "ubSMOTE")
  > 
  > # Cleaning the set.
  > train <- train %>%
  +   cleanse
  ── Checking unique value ─────────────────────────── unique value is one ──
  No variables that unique value is one.
  
  ── Checking unique rate ─────────────────────────────── high unique rate ──
  No variables that high unique rate.
  
  ── Checking character variables ─────────────────────── categorical data ──
  No character variables.
  
  > 
  > # Run the model fitting.
  > result <- run_models(.data = train, target = "Kyphosis", positive = "present")
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Error in `purrr::map()`:
  ℹ In index: 6.
  Caused by error in `process.y.margin.and.objective()`:
  ! Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
  Backtrace:
       ▆
    1. ├─alookr::run_models(.data = train, target = "Kyphosis", positive = "present")
    2. │ └─... %>% ...
    3. ├─tibble::tibble(...)
    4. │ └─tibble:::tibble_quos(xs, .rows, .name_repair)
    5. │   └─rlang::eval_tidy(xs[[j]], mask)
    6. ├─purrr::map(., ~future::value(.x))
    7. │ └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
    8. │   ├─purrr:::with_indexed_errors(...)
    9. │   │ └─base::withCallingHandlers(...)
   10. │   ├─purrr:::call_with_cleanup(...)
   11. │   └─alookr (local) .f(.x[[i]], ...)
   12. │     ├─future::value(.x)
   13. │     └─future:::value.Future(.x)
   14. │       └─future:::signalConditions(...)
   15. │         └─base::stop(condition)
   16. └─purrr (local) `<fn>`(`<smplErrr>`)
   17.   └─cli::cli_abort(...)
   18.     └─rlang::abort(...)
  Execution halted

Package: automatedRecLin
Check: examples
New result: ERROR
  Running examples in ‘automatedRecLin-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: custom_rec_lin_model
  > ### Title: Create a Custom Record Linkage Model
  > ### Aliases: custom_rec_lin_model
  > 
  > ### ** Examples
  > 
  > if (requireNamespace("xgboost", quietly = TRUE)) {
  +   df_1 <- data.frame(
  +     "name" = c("James", "Emma", "William", "Olivia", "Thomas",
  +     "Sophie", "Harry", "Amelia", "George", "Isabella"),
  +     "surname" = c("Smith", "Johnson", "Brown", "Taylor", "Wilson",
  +     "Davis", "Clark", "Harris", "Lewis", "Walker")
  +   )
  +   df_2 <- data.frame(
  +     "name" = c("James", "Ema", "Wimliam", "Olivia", "Charlotte",
  +     "Henry", "Lucy", "Edward", "Alice", "Jack"),
  +     "surname" = c("Smith", "Johnson", "Bron", "Tailor", "Moore",
  +     "Evans", "Hall", "Wright", "Green", "King")
  +   )
  +   comparators <- list("name" = jarowinkler_complement(),
  +                       "surname" = jarowinkler_complement())
  +   matches <- data.frame("a" = 1:4, "b" = 1:4)
  +   vectors <- comparison_vectors(A = df_1, B = df_2, variables = c("name", "surname"),
  +                                comparators = comparators, matches = matches)
  +   train_data <- xgboost::xgb.DMatrix(
  +     data = as.matrix(vectors$Omega[, c("gamma_name", "gamma_surname")]),
  +     label = vectors$Omega$match
  +   )
  +   params <- list(objective = "binary:logistic",
  +                  eval_metric = "logloss")
  +   model_xgb <- xgboost::xgboost(data = train_data, params = params,
  +                                 nrounds = 100, verbose = 0)
  +   custom_xgb_model <- custom_rec_lin_model(model_xgb, vectors)
  +   custom_xgb_model
  + }
  Warning in throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
    Parameter(s) have been removed from this function: params. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Error in xgboost::xgboost(data = train_data, params = params, nrounds = 100,  : 
    argument "y" is missing, with no default
  Calls: <Anonymous> -> process.y.margin.and.objective -> NROW
  Execution halted

Package: automatedRecLin
Check: tests
New result: ERROR
    Running ‘tinytest.R’ [0s/4s]
  Running the tests in ‘tests/tinytest.R’ failed.
  Complete output:
    > if ( requireNamespace("tinytest", quietly=TRUE) ){
    +   tinytest::test_package("automatedRecLin", ncpu = 1)
    + }
    starting worker pid=1144165 on localhost:11105 at 00:32:03.282
    
    test_comparators.R............    2 tests OK  68ms
    
    test_comparison_vectors.R.....    2 tests OK  52ms
    kliep function error: missing value where TRUE/FALSE needed 
    ========================================================
    kliep function error: missing value where TRUE/FALSE needed 
    ========================================================
    
    test_mec.R....................   11 tests OK  0.9s
    Error in checkForRemoteErrors(val) : 
      one node produced an error: argument "y" is missing, with no default
    Calls: <Anonymous> ... clusterApply -> staticClusterApply -> checkForRemoteErrors
    Warning messages:
    1: In check.sigma(nsigma, sigma_quantile, sigma, dist_nu) :
      There are duplicate values in 'sigma', only the unique values are used.
    
    2: In check.sigma(nsigma, sigma_quantile, sigma, dist_nu) :
      There are duplicate values in 'sigma', only the unique values are used.
    
    3: In check.sigma(nsigma, sigma_quantile, sigma, dist_nu) :
      There are duplicate values in 'sigma', only the unique values are used.
    
    4: In check.sigma(nsigma, sigma_quantile, sigma, dist_nu) :
      There are duplicate values in 'sigma', only the unique values are used.
    
    5: In check.sigma(nsigma, sigma_quantile, sigma, dist_nu) :
      There are duplicate values in 'sigma', only the unique values are used.
    
    6: In throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
      Parameter(s) have been removed from this function: params. This warning will become an error in a future version.
    7: In throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
      Passed unrecognized parameters: verbose. This warning will become an error in a future version.
    8: In throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
      Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
    Execution halted

Package: autostats
Check: examples
New result: ERROR
  Running examples in ‘autostats-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: auto_variable_contributions
  > ### Title: Plot Variable Contributions
  > ### Aliases: auto_variable_contributions
  > 
  > ### ** Examples
  > 
  > 
  > iris %>%
  + framecleaner::create_dummies() %>%
  + auto_variable_contributions(
  +  tidy_formula(., target = Petal.Width)
  +  )
  1 column(s) have become 3 dummy columns
  Error in if (validate & xgb_obj != "multi:softprob") { : 
    argument is of length zero
  Calls: %>% -> auto_variable_contributions
  Execution halted

Package: autostats
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘autostats.Rmd’ using rmarkdown
  
  Quitting from autostats.Rmd:40-43 [unnamed-chunk-3]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `if (validate & xgb_obj != "multi:softprob") ...`:
  ! argument is of length zero
  ---
  Backtrace:
      ▆
   1. ├─iris %>% auto_variable_contributions(species_formula)
   2. ├─autostats::auto_variable_contributions(., species_formula)
   3. │ ├─base::suppressWarnings(...)
   4. │ │ └─base::withCallingHandlers(...)
   5. │ └─data %>% tidy_xgboost(formula, validate = FALSE)
   6. └─autostats::tidy_xgboost(., formula, validate = FALSE)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'autostats.Rmd' failed with diagnostics:
  argument is of length zero
  --- failed re-building ‘autostats.Rmd’
  
  --- re-building ‘tidyXgboost.Rmd’ using rmarkdown
  
  Quitting from tidyXgboost.Rmd:78-89 [unnamed-chunk-4]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `xgb$feature_names <- nms`:
  ! ALTLIST classes must provide a Set_elt method [class: XGBAltrepPointerClass, pkg: xgboost]
  ---
  Backtrace:
      ▆
   1. ├─xgb_tuned_fit_grid %>% visualize_model()
   2. ├─autostats::visualize_model(.)
   3. └─autostats:::visualize_model.xgb.Booster(.)
   4.   └─autostats:::plot_varimp_xgboost(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'tidyXgboost.Rmd' failed with diagnostics:
  ALTLIST classes must provide a Set_elt method [class: XGBAltrepPointerClass, pkg: xgboost]
  --- failed re-building ‘tidyXgboost.Rmd’
  
  SUMMARY: processing the following files failed:
    ‘autostats.Rmd’ ‘tidyXgboost.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: BioMoR
Check: tests
New result: ERROR
    Running ‘testthat.R’ [111s/74s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(BioMoR)
    > 
    > test_check("BioMoR")
    Loading required namespace: randomForest
    Loading required package: ggplot2
    Loading required package: lattice
    Loading required package: dplyr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
        filter, lag
    
    The following objects are masked from 'package:base':
    
        intersect, setdiff, setequal, union
    
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stats':
    
        step
    
    randomForest 4.7-1.2
    Type rfNews() to see new features/changes/bug fixes.
    
    Attaching package: 'randomForest'
    
    The following object is masked from 'package:dplyr':
    
        combine
    
    The following object is masked from 'package:ggplot2':
    
        margin
    
    Setting direction: controls > cases
    note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
    
    ------------------------------------------------------------------------------
    You have loaded plyr after dplyr - this is likely to cause problems.
    If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
    library(plyr); library(dplyr)
    ------------------------------------------------------------------------------
    
    Attaching package: 'plyr'
    
    The following objects are masked from 'package:dplyr':
    
        arrange, count, desc, failwith, id, mutate, rename, summarise,
        summarize
    
    Saving _problems/test_models-35.R
    [ FAIL 1 | WARN 600 | SKIP 0 | PASS 5 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test_models.R:35:3'): XGB model trains and predicts ─────────────────
    Error in `{     if (!(length(ctrl$seeds) == 1L && is.na(ctrl$seeds)))          set.seed(ctrl$seeds[[iter]][parm])     loadNamespace("caret")     loadNamespace("recipes")     if (ctrl$verboseIter)          progress(printed[parm, , drop = FALSE], names(resampleIndex), iter)     if (names(resampleIndex)[iter] != "AllData") {         modelIndex <- resampleIndex[[iter]]         holdoutIndex <- ctrl$indexOut[[iter]]     }     else {         modelIndex <- 1:nrow(dat)         holdoutIndex <- modelIndex     }     if (testing)          cat("pre-model\n")     if (!is.null(info$submodels[[parm]]) && nrow(info$submodels[[parm]]) > 0) {         submod <- info$submodels[[parm]]     }     else submod <- NULL     mod_rec <- try(rec_model(rec, subset_x(dat, modelIndex), method = method, tuneValue = info$loop[parm, , drop = FALSE], obsLevels = lev, classProbs = ctrl$classProbs, sampling = ctrl$sampling, ...), silent = TRUE)     if (testing)          print(mod_rec)     if (!model_failed(mod_rec)) {         predicted <- try(rec_pred(method = method, object = mod_rec, newdata = subset_x(dat, holdoutIndex), param = submod), silent = TRUE)         if (pred_failed(predicted)) {             fail_warning(settings = printed[parm, , drop = FALSE], msg = predicted, where = "predictions", iter = names(resampleIndex)[iter], verb = ctrl$verboseIter)             predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod)         }     }     else {         fail_warning(settings = printed[parm, , drop = FALSE], msg = mod_rec, iter = names(resampleIndex)[iter], verb = ctrl$verboseIter)         predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod)     }     if (testing)          print(head(predicted))     if (ctrl$classProbs) {         if (!model_failed(mod_rec)) {             probValues <- rec_prob(method = method, object = mod_rec, newdata = subset_x(dat, holdoutIndex), param = submod)         }         else {             probValues <- fill_failed_prob(holdoutIndex, lev, submod)         }         if (testing)              print(head(probValues))     }     predicted <- trim_values(predicted, ctrl, is.null(lev))     ho_data <- holdout_rec(mod_rec, dat, holdoutIndex)     if (!is.null(submod)) {         allParam <- expandParameters(info$loop[parm, , drop = FALSE], submod)         allParam <- allParam[complete.cases(allParam), , drop = FALSE]         predicted <- lapply(predicted, function(x, lv, dat) {             x <- outcome_conversion(x, lv = lev)             dat$pred <- x             dat         }, lv = lev, dat = ho_data)         if (testing)              print(head(predicted))         if (ctrl$classProbs)              predicted <- mapply(cbind, predicted, probValues, SIMPLIFY = FALSE)         if (keep_pred) {             tmpPred <- predicted             for (modIndex in seq(along.with = tmpPred)) {                 tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex, , drop = FALSE], all = TRUE)             }             tmpPred <- rbind.fill(tmpPred)             tmpPred$Resample <- names(resampleIndex)[iter]         }         else tmpPred <- NULL         thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method)         if (testing)              print(head(thisResample))         if (length(lev) > 1 && length(lev) <= 50) {             cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs))             for (ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])         }         thisResample <- do.call("rbind", thisResample)         thisResample <- cbind(allParam, thisResample)     }     else {         pred_val <- outcome_conversion(predicted, lv = lev)         tmp <- ho_data         tmp$pred <- pred_val         if (ctrl$classProbs)              tmp <- cbind(tmp, probValues)         if (keep_pred) {             tmpPred <- tmp             tmpPred$rowIndex <- holdoutIndex             tmpPred <- merge(tmpPred, info$loop[parm, , drop = FALSE], all = TRUE)             tmpPred$Resample <- names(resampleIndex)[iter]         }         else tmpPred <- NULL         thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method)         if (length(lev) > 1 && length(lev) <= 50)              thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs))         thisResample <- as.data.frame(t(thisResample), stringsAsFactors = FALSE)         thisResample <- cbind(thisResample, info$loop[parm, , drop = FALSE])     }     thisResample$Resample <- names(resampleIndex)[iter]     thisResampleExtra <- optimism_rec(ctrl, dat, iter, lev, method, mod_rec, predicted, submod, info$loop[parm, , drop = FALSE])     if (ctrl$verboseIter)          progress(printed[parm, , drop = FALSE], names(resampleIndex), iter, FALSE)     if (testing)          print(thisResample)     list(resamples = thisResample, pred = tmpPred, resamplesExtra = thisResampleExtra) }`: task 1 failed - "$ operator is invalid for atomic vectors"
    Backtrace:
        ▆
     1. └─BioMoR::train_xgb_caret(df, "Label", ctrl) at test_models.R:35:3
     2.   ├─caret::train(...)
     3.   └─caret:::train.recipe(...)
     4.     └─caret:::train_rec(...)
     5.       └─... %op% ...
     6.         └─e$fun(obj, substitute(ex), parent.frame(), e$data)
    
    [ FAIL 1 | WARN 600 | SKIP 0 | PASS 5 ]
    Error:
    ! Test failures.
    Execution halted

Package: bundle
Check: examples
New result: ERROR
  Running examples in ‘bundle-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: bundle.model_fit
  > ### Title: Bundle a parsnip 'model_fit' object
  > ### Aliases: bundle.model_fit bundle_model_fit
  > 
  > ### ** Examples
  > 
  > ## Don't show: 
  > if (rlang::is_installed("parsnip") && rlang::is_installed("xgboost")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
  + ## End(Don't show)
  + # fit model and bundle ------------------------------------------------
  + library(parsnip)
  + library(xgboost)
  + 
  + set.seed(1)
  + 
  + mod <-
  +   boost_tree(trees = 5, mtry = 3) %>%
  +   set_mode("regression") %>%
  +   set_engine("xgboost") %>%
  +   fit(mpg ~ ., data = mtcars)
  + 
  + mod_bundle <- bundle(mod)
  + 
  + # then, after saveRDS + readRDS or passing to a new session ----------
  + mod_unbundled <- unbundle(mod_bundle)
  + 
  + mod_unbundled_preds <- predict(mod_unbundled, new_data = mtcars)
  + ## Don't show: 
  + }) # examplesIf
  > library(parsnip)
  > library(xgboost)
  > set.seed(1)
  > mod <- boost_tree(trees = 5, mtry = 3) %>% set_mode("regression") %>% 
  +     set_engine("xgboost") %>% fit(mpg ~ ., data = mtcars)
  > mod_bundle <- bundle(mod)
  > mod_unbundled <- unbundle(mod_bundle)
  Error in xgboost::xgb.load.raw(object, as_booster = TRUE) : 
    unused argument (as_booster = TRUE)
  Calls: <Anonymous> ... <Anonymous> -> unbundle -> unbundle.bundle -> <Anonymous>
  Execution halted

Package: bundle
Check: tests
New result: ERROR
    Running ‘testthat.R’ [103s/103s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/tests.html
    > # * https://testthat.r-lib.org/reference/test_package.html#special-files
    > 
    > library(testthat)
    > library(bundle)
    > 
    > test_check("bundle")
    Loading required package: ggplot2
    Loading required package: lattice
    
    Attaching package: 'parsnip'
    
    The following object is masked from 'package:dbarts':
    
        bart
    
    Saving _problems/test_bundle_parsnip-47.R
    Loading required package: dplyr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
        filter, lag
    
    The following objects are masked from 'package:base':
    
        intersect, setdiff, setequal, union
    
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stats':
    
        step
    
    Saving _problems/test_bundle_workflows-69.R
    Saving _problems/test_bundle_xgboost-33.R
    Saving _problems/test_utils-43.R
    [ FAIL 4 | WARN 0 | SKIP 11 | PASS 24 ]
    
    ══ Skipped tests (11) ══════════════════════════════════════════════════════════
    • !interactive() is TRUE (4): 'test_bundle_h2o.R:137:3',
      'test_bundle_h2o.R:284:3', 'test_bundle_h2o.R:428:3',
      'test_bundle_h2o.R:564:3'
    • On CRAN (7): 'test_bundle_bart.R:100:1', 'test_bundle_bart.R:106:1',
      'test_bundle_embed.R:2:3', 'test_bundle_h2o.R:2:3',
      'test_bundle_keras.R:2:3', 'test_bundle_torch.R:6:3',
      'test_bundle_workflows.R:126:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test_bundle_parsnip.R:34:3'): bundling + unbundling parsnip model_fits (xgboost) ──
    <callr_status_error/callr_error/rlib_error_3_0/rlib_error/error/condition>
    Error: ! in callr subprocess.
    Caused by error in `xgboost::xgb.load.raw(object, as_booster = TRUE)`:
    ! unused argument (as_booster = TRUE)
    Backtrace:
        ▆
     1. └─callr::r(...) at test_bundle_parsnip.R:34:3
     2.   └─callr:::get_result(output = out, options)
     3.     └─throw(callr_remote_error(remerr, output), parent = fix_msg(remerr[[3]]))
    ── Error ('test_bundle_workflows.R:53:3'): bundling + unbundling tidymodels workflows (xgboost + step_log) ──
    <callr_status_error/callr_error/rlib_error_3_0/rlib_error/error/condition>
    Error: ! in callr subprocess.
    Caused by error in `xgboost::xgb.load.raw(object, as_booster = TRUE)`:
    ! unused argument (as_booster = TRUE)
    Backtrace:
        ▆
     1. └─callr::r(...) at test_bundle_workflows.R:53:3
     2.   └─callr:::get_result(output = out, options)
     3.     └─throw(callr_remote_error(remerr, output), parent = fix_msg(remerr[[3]]))
    ── Error ('test_bundle_xgboost.R:23:3'): bundling + unbundling xgboost fits ────
    <callr_status_error/callr_error/rlib_error_3_0/rlib_error/error/condition>
    Error: ! in callr subprocess.
    Caused by error in `process.y.margin.and.objective(y, base_margin, objective, params)`:
    ! Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. └─callr::r(...) at test_bundle_xgboost.R:23:3
     2.   └─callr:::get_result(output = out, options)
     3.     └─throw(callr_remote_error(remerr, output), parent = fix_msg(remerr[[3]]))
    ── Error ('test_utils.R:43:3'): swap_element works ─────────────────────────────
    Error in `xgboost::xgb.load.raw(object, as_booster = TRUE)`: unused argument (as_booster = TRUE)
    Backtrace:
        ▆
     1. └─bundle::swap_element(res, "fit") at test_utils.R:43:3
     2.   ├─bundle::unbundle(component)
     3.   └─bundle:::unbundle.bundle(component)
     4.     └─x$situate(get_object(x))
    
    [ FAIL 4 | WARN 0 | SKIP 11 | PASS 24 ]
    Error:
    ! Test failures.
    Execution halted

Package: butcher
Check: examples
New result: ERROR
  Running examples in ‘butcher-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: axe-flexsurvreg
  > ### Title: Axing an flexsurvreg.
  > ### Aliases: axe-flexsurvreg axe_call.flexsurvreg axe_env.flexsurvreg
  > 
  > ### ** Examples
  > 
  > ## Don't show: 
  > if (rlang::is_installed("flexsurv")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
  + ## End(Don't show)
  + # Load libraries
  + library(parsnip)
  + library(flexsurv)
  + 
  + # Create model and fit
  + flexsurvreg_fit <- surv_reg(mode = "regression", dist = "gengamma") %>%
  +   set_engine("flexsurv") %>%
  +   fit(Surv(Tstart, Tstop, status) ~ trans, data = bosms3)
  + 
  + out <- butcher(flexsurvreg_fit, verbose = TRUE)
  + 
  + # Another flexsurvreg model object
  + wrapped_flexsurvreg <- function() {
  +   some_junk_in_environment <- runif(1e6)
  +   fit <- flexsurvreg(Surv(futime, fustat) ~ 1,
  +                      data = ovarian, dist = "weibull")
  +   return(fit)
  + }
  + 
  + out <- butcher(wrapped_flexsurvreg(), verbose = TRUE)
  + ## Don't show: 
  + }) # examplesIf
  > library(parsnip)
  > library(flexsurv)
  Loading required package: survival
  > flexsurvreg_fit <- surv_reg(mode = "regression", dist = "gengamma") %>% 
  +     set_engine("flexsurv") %>% fit(Surv(Tstart, Tstop, status) ~ trans, data = bosms3)
  Error:
  ! `surv_reg()` was deprecated in parsnip 1.4.0 and is now defunct.
  ℹ Please use `survival_reg()` instead.
  Backtrace:
       ▆
    1. ├─(if (getRversion() >= "3.4") withAutoprint else force)(...)
    2. │ └─base::source(...)
    3. │   ├─base::withVisible(eval(ei, envir))
    4. │   └─base::eval(ei, envir)
    5. │     └─base::eval(ei, envir)
    6. ├─... %>% ...
    7. ├─generics::fit(., Surv(Tstart, Tstop, status) ~ trans, data = bosms3)
    8. ├─parsnip::set_engine(., "flexsurv")
    9. └─parsnip::surv_reg(mode = "regression", dist = "gengamma")
   10.   └─lifecycle::deprecate_stop("1.4.0", "surv_reg()", "survival_reg()")
   11.     └─lifecycle:::deprecate_stop0(msg)
   12.       └─rlang::cnd_signal(...)
  Execution halted

Package: butcher
Check: tests
New result: ERROR
    Running ‘testthat.R’ [21s/20s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(butcher)
    > 
    > test_check("butcher")
    1 package (dimRed) is needed for this step but is not installed.
    To install run: `install.packages("dimRed")`
    Saving _problems/test-xrf-12.R
    Saving _problems/test-xrf-25.R
    Saving _problems/test-xrf-40.R
    Saving _problems/test-xrf-58.R
    [ FAIL 4 | WARN 16 | SKIP 35 | PASS 196 ]
    
    ══ Skipped tests (35) ══════════════════════════════════════════════════════════
    • On CRAN (34): 'test-c5.R:2:3', 'test-earth.R:2:3', 'test-earth.R:31:3',
      'test-elnet.R:2:3', 'test-flexsurvreg.R:2:3', 'test-flexsurvreg.R:15:3',
      'test-flexsurvreg.R:43:3', 'test-gausspr.R:2:3', 'test-glmnet.R:2:3',
      'test-kknn.R:2:3', 'test-klaR.R:2:3', 'test-klaR.R:17:3', 'test-ksvm.R:2:3',
      'test-mda.R:2:3', 'test-mda.R:23:3', 'test-mda.R:95:3',
      'test-mixOmics.R:2:3', 'test-mixOmics.R:21:3', 'test-mixOmics.R:38:3',
      'test-multnet.R:2:3', 'test-nnet.R:2:3', 'test-randomForest.R:2:3',
      'test-rpart.R:2:3', 'test-rpart.R:18:3', 'test-sclass.R:2:3',
      'test-survreg.R:2:3', 'test-survreg.penal.R:2:3', 'test-train.R:2:3',
      'test-train.R:42:3', 'test-train.recipe.R:9:3', 'test-ui.R:1:1',
      'test-weigh.R:1:1', 'test-xgb.R:6:3', 'test-xgb.R:34:3'
    • {mixOmics} is not installed (1): 'test-recipe.R:419:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Failure ('test-xrf.R:12:3'): xrf + axe_call() works ─────────────────────────
    Expected `x$xgb$call` to equal `rlang::expr(dummy_call())`.
    Differences:
    `actual` is NULL
    `expected` is a call
    
    ── Error ('test-xrf.R:25:3'): xrf + axe_env() works ────────────────────────────
    Error in `x$callbacks <- purrr::map(x$callbacks, function(x) as.function(c(formals(x), body(x)), env = rlang::base_env()))`: ALTLIST classes must provide a Set_elt method [class: XGBAltrepPointerClass, pkg: xgboost]
    Backtrace:
        ▆
     1. ├─butcher::axe_env(res) at test-xrf.R:25:3
     2. └─butcher:::axe_env.xrf(res)
     3.   ├─butcher::axe_env(res$xgb, ...)
     4.   └─butcher:::axe_env.xgb.Booster(res$xgb, ...)
    ── Error ('test-xrf.R:40:3'): xrf + butcher() works ────────────────────────────
    Error in `x$callbacks <- purrr::map(x$callbacks, function(x) as.function(c(formals(x), body(x)), env = rlang::base_env()))`: ALTLIST classes must provide a Set_elt method [class: XGBAltrepPointerClass, pkg: xgboost]
    Backtrace:
        ▆
     1. └─butcher::butcher(res) at test-xrf.R:40:3
     2.   ├─butcher::axe_env(x, verbose = FALSE, ...)
     3.   └─butcher:::axe_env.xrf(x, verbose = FALSE, ...)
     4.     ├─butcher::axe_env(res$xgb, ...)
     5.     └─butcher:::axe_env.xgb.Booster(res$xgb, ...)
    ── Error ('test-xrf.R:58:3'): xrf + predict() works ────────────────────────────
    Error in `x$callbacks <- purrr::map(x$callbacks, function(x) as.function(c(formals(x), body(x)), env = rlang::base_env()))`: ALTLIST classes must provide a Set_elt method [class: XGBAltrepPointerClass, pkg: xgboost]
    Backtrace:
        ▆
     1. └─butcher::butcher(res) at test-xrf.R:58:3
     2.   ├─butcher::axe_env(x, verbose = FALSE, ...)
     3.   └─butcher:::axe_env.xrf(x, verbose = FALSE, ...)
     4.     ├─butcher::axe_env(res$xgb, ...)
     5.     └─butcher:::axe_env.xgb.Booster(res$xgb, ...)
    
    [ FAIL 4 | WARN 16 | SKIP 35 | PASS 196 ]
    Error:
    ! Test failures.
    Execution halted

Package: CausalGPS
Check: tests
New result: ERROR
    Running ‘testthat.R’ [11s/11s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(CausalGPS)
    > 
    > Sys.setenv("R_TESTS" = "")
    > library(testthat)
    > test_check("CausalGPS")
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_erf-22.R
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_gps-29.R
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-generate_pseudo_pop-35.R
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-trim_it-34.R
    [ FAIL 4 | WARN 200 | SKIP 19 | PASS 68 ]
    
    ══ Skipped tests (19) ══════════════════════════════════════════════════════════
    • On CRAN (17): 'test-CausalGPS_smooth.R:3:4', 'test-absolute_corr_fun.R:4:3',
      'test-absolute_weighted_corr_fun.R:4:3', 'test-check_covar_balance.R:2:1',
      'test-check_kolmogorov_smirnov.R:2:3', 'test-compile_pseudo_pop.R:3:3',
      'test-compute_density.R:3:3', 'test-compute_minmax.R:3:3',
      'test-compute_resid.R:3:3', 'test-create_weighting.R:3:3',
      'test-estimate_gps.R:63:3', 'test-estimate_gps.R:92:3',
      'test-estimate_hat_vals.R:3:3', 'test-matching_fn.R:3:3',
      'test-set_logger.R:3:5', 'test-train_it.R:3:3', 'test-train_it.R:18:3'
    • empty test (2): 'test-estimate_npmetric_erf.R:1:1',
      'test-estimate_npmetric_erf.R:89:1'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-estimate_erf.R:19:3'): estimate erf works as expected ──────────
    Error in `SuperLearner::SuperLearner(Y = target, X = data.frame(input), SL.library = sl_lib_internal)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─CausalGPS::estimate_gps(...) at test-estimate_erf.R:19:3
     2.   └─CausalGPS:::train_it(...)
     3.     └─SuperLearner::SuperLearner(...)
    ── Error ('test-estimate_gps.R:26:3'): estimate_gps works as expected. ─────────
    Error in `SuperLearner::SuperLearner(Y = target, X = data.frame(input), SL.library = sl_lib_internal)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─CausalGPS::estimate_gps(...) at test-estimate_gps.R:26:3
     2.   └─CausalGPS:::train_it(...)
     3.     └─SuperLearner::SuperLearner(...)
    ── Error ('test-generate_pseudo_pop.R:31:3'): generate_pseudo_pop works as expected. ──
    Error in `SuperLearner::SuperLearner(Y = target, X = data.frame(input), SL.library = sl_lib_internal)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─CausalGPS::estimate_gps(...) at test-generate_pseudo_pop.R:31:3
     2.   └─CausalGPS:::train_it(...)
     3.     └─SuperLearner::SuperLearner(...)
    ── Error ('test-trim_it.R:31:3'): trim_it works as expected ────────────────────
    Error in `SuperLearner::SuperLearner(Y = target, X = data.frame(input), SL.library = sl_lib_internal)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─CausalGPS::estimate_gps(...) at test-trim_it.R:31:3
     2.   └─CausalGPS:::train_it(...)
     3.     └─SuperLearner::SuperLearner(...)
    
    [ FAIL 4 | WARN 200 | SKIP 19 | PASS 68 ]
    Error:
    ! Test failures.
    Execution halted

Package: CRE
Check: tests
New result: ERROR
    Running ‘testthat.R’ [5s/5s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(CRE)
    > 
    > test_check("CRE")
    Error in xgboost::xgboost(data = xgmat, objective = "binary:logistic",  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = "binary:logistic",  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_cate-165.R
    [ FAIL 1 | WARN 10 | SKIP 25 | PASS 4 ]
    
    ══ Skipped tests (25) ══════════════════════════════════════════════════════════
    • On CRAN (25): 'test-check_input_data.R:3:3', 'test-cre.R:3:3',
      'test-discover_rules.R:3:3', 'test-estimate_cate.R:3:3',
      'test-estimate_ite.R:4:3', 'test-estimate_ite_aipw.R:3:3',
      'test-estimate_ite_bart.R:4:3', 'test-estimate_ite_cf.R:4:3',
      'test-estimate_ite_poisson.R:3:3', 'test-estimate_ite_slearner.R:3:3',
      'test-estimate_ite_tlearner.R:3:3', 'test-estimate_ite_xlearner.R:3:3',
      'test-estimate_ps.R:3:3', 'test-evaluate.R:3:3',
      'test-extract_effect_modifiers.R:2:3', 'test-extract_rules.R:4:3',
      'test-filter_correlated_rules.R:3:3', 'test-filter_extreme_rules.R:3:3',
      'test-filter_irrelevant_rules.R:4:3', 'test-generate_cre_dataset.R:4:3',
      'test-generate_rules.R:3:3', 'test-generate_rules_matrix.R:3:3',
      'test-honest_splitting.R:3:3', 'test-interpret_rules.R:3:3',
      'test-predict.R:3:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-estimate_cate.R:162:3'): CATE Estimation Runs Correctly (test 2/2) ──
    Error in `SuperLearner::SuperLearner(Y = z, X = as.data.frame(X), newX = as.data.frame(X), family = binomial(), SL.library = ps_method, cvControl = list(V = 0))`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─CRE:::estimate_ite(...) at test-estimate_cate.R:162:3
     2.   └─CRE:::estimate_ite_aipw(y, z, X, learner_ps, learner_y)
     3.     └─CRE:::estimate_ps(z, X, learner_ps)
     4.       └─SuperLearner::SuperLearner(...)
    
    [ FAIL 1 | WARN 10 | SKIP 25 | PASS 4 ]
    Error:
    ! Test failures.
    Execution halted

Package: csmpv
Check: examples
New result: ERROR
  Running examples in ‘csmpv-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: LASSO2_XGBtraining
  > ### Title: Variable Selection with LASSO2 and Modeling with XGBoost
  > ### Aliases: LASSO2_XGBtraining
  > 
  > ### ** Examples
  > 
  > # Load in data sets:
  > data("datlist", package = "csmpv")
  > tdat = datlist$training
  > 
  > # The function saves files locally. You can define your own temporary directory. 
  > # If not, tempdir() can be used to get the system's temporary directory.
  > temp_dir = tempdir()
  > # As an example, let's define Xvars, which will be used later:
  > Xvars = c("highIPI", "B.Symptoms", "MYC.IHC", "BCL2.IHC", "CD10.IHC", "BCL6.IHC")
  > # The function can work with three different outcome types. 
  > # Here, we use binary as an example:
  > blxfit = LASSO2_XGBtraining(data = tdat, biomks = Xvars, Y = "DZsig",
  +                            outfile = paste0(temp_dir, "/binary_LASSO2_XGBoost"))
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'gamma' has been renamed to 'min_split_loss'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Error in xgboost::xgboost(objective = "binary:logistic", data = Dtrain,  : 
    argument "y" is missing, with no default
  Calls: LASSO2_XGBtraining ... <Anonymous> -> process.y.margin.and.objective -> NROW
  Execution halted

Package: csmpv
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘csmpv_vignette.rmd’ using rmarkdown
  
  Quitting from csmpv_vignette.rmd:421-425 [unnamed-chunk-34]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `xgboost::xgboost()`:
  ! argument "y" is missing, with no default
  ---
  Backtrace:
      ▆
   1. └─csmpv::XGBtraining(...)
   2.   └─xgboost::xgboost(...)
   3.     └─xgboost:::process.y.margin.and.objective(...)
   4.       └─base::NROW(y)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'csmpv_vignette.rmd' failed with diagnostics:
  argument "y" is missing, with no default
  --- failed re-building ‘csmpv_vignette.rmd’
  
  SUMMARY: processing the following file failed:
    ‘csmpv_vignette.rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: CytoProfile
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘getting_started.Rmd’ using rmarkdown
  Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps
  Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps
  
  Quitting from getting_started.Rmd:316-338 [ML1]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `confusionMatrix.default()`:
  ! The data must contain some levels that overlap the reference.
  ---
  Backtrace:
      ▆
   1. └─CytoProfile::cyt_xgb(...)
   2.   ├─caret::confusionMatrix(as.factor(cv_pred_labels), as.factor(actual_labels))
   3.   └─caret:::confusionMatrix.default(as.factor(cv_pred_labels), as.factor(actual_labels))
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'getting_started.Rmd' failed with diagnostics:
  The data must contain some levels that overlap the reference.
  --- failed re-building ‘getting_started.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘getting_started.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: DALEXtra
Check: examples
New result: ERROR
  Running examples in ‘DALEXtra-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: explain_xgboost
  > ### Title: Create explainer from your xgboost model
  > ### Aliases: explain_xgboost
  > 
  > ### ** Examples
  > 
  > library("xgboost")
  > library("DALEXtra")
  > library("mlr")
  Loading required package: ParamHelpers
  > # 8th column is target that has to be omitted in X data
  > data <- as.matrix(createDummyFeatures(titanic_imputed[,-8]))
  > model <- xgboost(data, titanic_imputed$survived, nrounds = 10,
  +                  params = list(objective = "binary:logistic"),
  +                 prediction = TRUE)
  Warning in throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
    Parameter(s) have been removed from this function: params. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: prediction. This warning will become an error in a future version.
  > # explainer with encode functiom
  > explainer_1 <- explain_xgboost(model, data = titanic_imputed[,-8],
  +                                titanic_imputed$survived,
  +                                encode_function = function(data) {
  +  as.matrix(createDummyFeatures(data))
  + })
  Preparation of a new explainer is initiated
    -> model label       :  xgb.Booster  (  default  )
    -> data              :  2207  rows  7  cols 
    -> target variable   :  2207  values 
    -> predict function  :  yhat.xgb.Booster  will be used (  default  )
    -> predicted values  :  No value for predict function target column. (  default  )
  Error in strsplit(model$params$objective, ":", fixed = TRUE) : 
    non-character argument
  Calls: explain_xgboost ... explain -> model_info -> model_info.xgb.Booster -> strsplit
  Execution halted

Package: DALEXtra
Check: tests
New result: ERROR
    Running ‘testthat.R’ [171s/168s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(DALEXtra)
    Loading required package: DALEX
    Welcome to DALEX (version: 2.5.3).
    Find examples and detailed introduction at: http://ema.drwhy.ai/
    Additional features will be available after installation of: ggpubr.
    Use 'install_dependencies()' to get all suggested dependencies
    > 
    > test_check("DALEXtra")
    Preparation of a new explainer is initiated
      -> model label       :  LM 
      -> data              :  9000  rows  6  cols 
      -> target variable   :  9000  values 
      -> predict function  :  yhat.WrappedModel  will be used (  default  )
      -> predicted values  :  No value for predict function target column. (  default  )
      -> model_info        :  package mlr , ver. 2.19.3 , task regression (  default  ) 
      -> predicted values  :  numerical, min =  1792.597 , mean =  3506.836 , max =  6241.447  
      -> residual function :  difference between y and yhat (  default  )
      -> residuals         :  numerical, min =  -257.2555 , mean =  4.687686 , max =  472.356  
      A new explainer has been created!  
    Preparation of a new explainer is initiated
      -> model label       :  RF 
      -> data              :  9000  rows  6  cols 
      -> target variable   :  9000  values 
      -> predict function  :  yhat.WrappedModel  will be used (  default  )
      -> predicted values  :  No value for predict function target column. (  default  )
      -> model_info        :  package mlr , ver. 2.19.3 , task regression (  default  ) 
      -> predicted values  :  numerical, min =  1811.874 , mean =  3503.909 , max =  6251.841  
      -> residual function :  difference between y and yhat (  default  )
      -> residuals         :  numerical, min =  -551.8409 , mean =  7.614965 , max =  784.4979  
      A new explainer has been created!  
    Preparation of a new explainer is initiated
      -> model label       :  GBM 
      -> data              :  9000  rows  6  cols 
      -> target variable   :  9000  values 
      -> predict function  :  yhat.WrappedModel  will be used (  default  )
      -> predicted values  :  No value for predict function target column. (  default  )
      -> model_info        :  package mlr , ver. 2.19.3 , task regression (  default  ) 
      -> predicted values  :  numerical, min =  2121.711 , mean =  3502.64 , max =  6055.528  
      -> residual function :  difference between y and yhat (  default  )
      -> residuals         :  numerical, min =  -518.7114 , mean =  8.883995 , max =  747.0608  
      A new explainer has been created!  
    additional arguments ignored in warning()
    Preparation of a new explainer is initiated
      -> model label       :  ranger  (  default  )
      -> data              :  2207  rows  7  cols 
      -> target variable   :  2207  values 
      -> predict function  :  yhat.ranger  will be used (  default  )
      -> predicted values  :  No value for predict function target column. (  default  )
      -> model_info        :  package ranger , ver. 0.17.0 , task classification (  default  ) 
      -> predicted values  :  numerical, min =  0.01717233 , mean =  0.3218475 , max =  0.9895025  
      -> residual function :  difference between y and yhat (  default  )
      -> residuals         :  numerical, min =  -0.7852652 , mean =  0.0003092278 , max =  0.8834231  
      A new explainer has been created!  
    additional arguments ignored in warning()
    Saving _problems/test_xgboost_explain-13.R
    Saving _problems/test_xgboost_explain-31.R
    Saving _problems/test_xgboost_explain-50.R
    [ FAIL 3 | WARN 15 | SKIP 11 | PASS 44 ]
    
    ══ Skipped tests (11) ══════════════════════════════════════════════════════════
    • Conda test env needed for tests (6): 'test_create_env.R:6:3',
      'test_create_env.R:27:3', 'test_create_env.R:40:3',
      'test_keras_explain.R:6:2', 'test_scikitlearn_explain.R:6:3',
      'tests_prints.R:8:3'
    • JAVA entry needed for tests (4): 'test_h2o_explain.R:8:3',
      'test_h2o_explain.R:33:3', 'test_h2o_explain.R:56:3',
      'test_h2o_explain.R:90:3'
    • Test with windows (1): 'test_champion_challenger.R:5:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test_xgboost_explain.R:11:3'): creating explainer classif ───────────
    Error in `strsplit(model$params$objective, ":", fixed = TRUE)`: non-character argument
    Backtrace:
        ▆
     1. └─DALEXtra::explain_xgboost(...) at test_xgboost_explain.R:11:3
     2.   └─DALEX::explain(...)
     3.     ├─DALEX::model_info(model, is_multiclass = task_subtype)
     4.     └─DALEXtra:::model_info.xgb.Booster(model, is_multiclass = task_subtype)
     5.       └─base::strsplit(model$params$objective, ":", fixed = TRUE)
    ── Error ('test_xgboost_explain.R:29:3'): creating explainer regr ──────────────
    Error in `strsplit(model$params$objective, ":", fixed = TRUE)`: non-character argument
    Backtrace:
        ▆
     1. └─DALEXtra::explain_xgboost(...) at test_xgboost_explain.R:29:3
     2.   └─DALEX::explain(...)
     3.     ├─DALEX::model_info(model, is_multiclass = task_subtype)
     4.     └─DALEXtra:::model_info.xgb.Booster(model, is_multiclass = task_subtype)
     5.       └─base::strsplit(model$params$objective, ":", fixed = TRUE)
    ── Error ('test_xgboost_explain.R:48:3'): creating explainer multi ─────────────
    Error in `strsplit(model$params$objective, ":", fixed = TRUE)`: non-character argument
    Backtrace:
        ▆
     1. └─DALEXtra::explain_xgboost(...) at test_xgboost_explain.R:48:3
     2.   └─DALEX::explain(...)
     3.     ├─DALEX::model_info(model, is_multiclass = task_subtype)
     4.     └─DALEXtra:::model_info.xgb.Booster(model, is_multiclass = task_subtype)
     5.       └─base::strsplit(model$params$objective, ":", fixed = TRUE)
    
    [ FAIL 3 | WARN 15 | SKIP 11 | PASS 44 ]
    Error:
    ! Test failures.
    Execution halted

Package: dblr
Check: examples
New result: ERROR
  Running examples in ‘dblr-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: dblr_train
  > ### Title: Discrete Boosting Logistic Regression Training
  > ### Aliases: dblr_train
  > 
  > ### ** Examples
  > 
  > # use iris data for example
  > dat <- iris
  > # create two categorical variables
  > dat$Petal.Width <- as.factor((iris$Petal.Width<=0.2)*1+(iris$Petal.Width>1.0)*2)
  > dat$Sepal.Length <- (iris$Sepal.Length<=3.0)*2+(iris$Sepal.Length>6.0)*1.25
  > # create the response variable
  > dat$Species <- as.numeric(dat$Species=='versicolor')
  > set.seed(123)
  > # random sampling
  > index <- sample(1:150,100,replace = FALSE)
  > # train the dblr model using the training data
  > dblr_fit <- dblr_train(train_x=dat[index,c(1:4)],
  + train_y=dat[index,5],category_cols = c('Petal.Width','Sepal.Length'),
  + metric = 'logloss',subsample = 0.5,eta = 0.05,colsample = 1.0,
  + lambda = 1.0,cv_early_stops = 10,verbose=FALSE)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: metrics. This warning will become an error in a future version.
  Error in begin_iteration:end_iteration : argument of length 0
  Calls: dblr_train -> <Anonymous>
  Execution halted

Package: ddml
Check: tests
New result: ERROR
    Running ‘testthat.R’ [34s/34s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(ddml)
    > 
    > test_check("ddml")
    Saving _problems/test-ml_wrappers-35.R
    
    sample fold 1/3
    sample fold 2/3
    sample fold 3/3[ FAIL 1 | WARN 6 | SKIP 0 | PASS 134 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-ml_wrappers.R:35:3'): mdl_xgboost is working ───────────────────
    Error in `process.y.margin.and.objective(as.vector(y), base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. └─ddml::mdl_xgboost(y, X, objective = "binary:logistic") at test-ml_wrappers.R:35:3
     2.   └─xgboost::xgboost(...)
     3.     └─xgboost:::process.y.margin.and.objective(...)
     4.       └─xgboost:::process.y.margin.and.objective(...)
    
    [ FAIL 1 | WARN 6 | SKIP 0 | PASS 134 ]
    Error:
    ! Test failures.
    Execution halted

Package: E2E
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘advanced-features.Rmd’ using rmarkdown
  
  Quitting from advanced-features.Rmd:102-113 [unnamed-chunk-5]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `bagging_dia()`:
  ! No base models were successfully trained or made valid predictions. Cannot perform bagging.
  ---
  Backtrace:
      ▆
   1. └─E2E::bagging_dia(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'advanced-features.Rmd' failed with diagnostics:
  No base models were successfully trained or made valid predictions. Cannot perform bagging.
  --- failed re-building ‘advanced-features.Rmd’
  
  --- re-building ‘diagnostic-workflow.Rmd’ using rmarkdown
  
  Quitting from diagnostic-workflow.Rmd:80-85 [unnamed-chunk-6]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `bagging_dia()`:
  ! No base models were successfully trained or made valid predictions. Cannot perform bagging.
  ---
  Backtrace:
      ▆
   1. └─E2E::bagging_dia(train_dia, base_model_name = "xb", n_estimators = 5)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'diagnostic-workflow.Rmd' failed with diagnostics:
  No base models were successfully trained or made valid predictions. Cannot perform bagging.
  --- failed re-building ‘diagnostic-workflow.Rmd’
  
  --- re-building ‘getting-started.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘getting-started.Rmd’
  
  --- re-building ‘integrated-pipeline.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘integrated-pipeline.Rmd’
  
  --- re-building ‘parameter-reference.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘parameter-reference.Rmd’
  
  --- re-building ‘prognostic-workflow.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘prognostic-workflow.Rmd’
  
  SUMMARY: processing the following files failed:
    ‘advanced-features.Rmd’ ‘diagnostic-workflow.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: EIX
Check: whether package can be installed
New result: ERROR
  Installation failed.

Package: explore
Check: examples
New result: ERROR
  Running examples in ‘explore-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: explain_xgboost
  > ### Title: Explain a binary target using xgboost
  > ### Aliases: explain_xgboost
  > 
  > ### ** Examples
  > 
  > data <- use_data_iris()
  > data$is_versicolor <- ifelse(data$Species == "versicolor", 1, 0)
  > data$Species <- NULL
  > explain_xgboost(data, target = is_versicolor, log = FALSE)
  Warning in check.deprecation(deprecated_cv_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric, nthread. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Error in all_nrounds[k] <- cv$best_iteration : 
    replacement has length zero
  Calls: explain_xgboost
  Execution halted

Package: explore
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘abtest.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘abtest.Rmd’
  
  --- re-building ‘clean-drop.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘clean-drop.Rmd’
  
  --- re-building ‘data.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘data.Rmd’
  
  --- re-building ‘describe.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘describe.Rmd’
  
  --- re-building ‘explain.Rmd’ using rmarkdown
  
  Quitting from explain.Rmd:65-69 [unnamed-chunk-6]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `all_nrounds[k] <- cv$best_iteration`:
  ! replacement has length zero
  ---
  Backtrace:
      ▆
   1. └─explore::explain_xgboost(data %>% drop_var_not_numeric(), target = buy)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'explain.Rmd' failed with diagnostics:
  replacement has length zero
  --- failed re-building ‘explain.Rmd’
  
  --- re-building ‘explore-mtcars.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘explore-mtcars.Rmd’
  
  --- re-building ‘explore-penguins.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘explore-penguins.Rmd’
  
  --- re-building ‘explore-titanic.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘explore-titanic.Rmd’
  
  --- re-building ‘explore.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘explore.Rmd’
  
  --- re-building ‘predict.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘predict.Rmd’
  
  --- re-building ‘report-target.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘report-target.Rmd’
  
  --- re-building ‘report-targetpct.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘report-targetpct.Rmd’
  
  --- re-building ‘report.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘report.Rmd’
  
  --- re-building ‘tips-tricks.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘tips-tricks.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘explain.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: FastRet
Check: tests
New result: ERROR
    Running ‘testthat.R’ [38s/22s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
    > # * https://testthat.r-lib.org/articles/special-files.html
    > 
    > library(testthat)
    > library(FastRet)
    > 
    > test_check("FastRet")
    Starting 2 test processes.
    Saving _problems/test-train_frm-gbtree-11.R
    Saving _problems/test-fit_gbtree-8.R
    Saving _problems/test-fit_gbtree-16.R
    > test-plot_frm.R: 2025-12-02 00:25:25.13 Starting training of a lasso model
    > test-plot_frm.R: 2025-12-02 00:25:25.13 Mocking is enabled. Returning 'mockdata/lasso_model.rds'
    > test-plot_frm.R: 2025-12-02 00:25:25.30 Starting training of a lasso model
    > test-plot_frm.R: 2025-12-02 00:25:25.30 Mocking is enabled. Returning 'mockdata/lasso_model.rds'
    > test-plot_frm.R: 2025-12-02 00:25:25.31 Starting model Adjustment
    > test-plot_frm.R: 2025-12-02 00:25:25.31 dim(original_data): 442 x 126
    > test-adjust_frm.R: 2025-12-02 00:25:25.69 Starting model Adjustment
    > test-adjust_frm.R: 2025-12-02 00:25:25.69 dim(original_data): 442 x 126
    > test-adjust_frm.R: 2025-12-02 00:25:25.69 dim(new_data): 25 x 3
    > test-adjust_frm.R: 2025-12-02 00:25:25.69 predictors: 1, 2
    > test-adjust_frm.R: 2025-12-02 00:25:25.69 nfolds: 5
    > test-adjust_frm.R: 2025-12-02 00:25:25.69 Preprocessing data
    > test-adjust_frm.R: 2025-12-02 00:25:25.70 Formula: RT_ADJ ~ RT + I(RT^2)
    > test-adjust_frm.R: 2025-12-02 00:25:25.70 Estimating performance of adjusted model in CV
    > test-adjust_frm.R: 2025-12-02 00:25:25.77 Fitting adjustment model on full new data set
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 Returning adjusted frm object
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 Starting model Adjustment
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 dim(original_data): 442 x 126
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 dim(new_data): 25 x 3
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 predictors: 1, 2, 3, 4, 5, 6
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 nfolds: 5
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 Preprocessing data
    > test-adjust_frm.R: 2025-12-02 00:25:25.78 Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT)
    > test-adjust_frm.R: 2025-12-02 00:25:25.79 Estimating performance of adjusted model in CV
    > test-adjust_frm.R: 2025-12-02 00:25:25.84 Fitting adjustment model on full new data set
    > test-adjust_frm.R: 2025-12-02 00:25:25.85 Returning adjusted frm object
    > test-selective_measuring.R: 2025-12-02 00:25:26.26 Starting Selective Measuring
    > test-selective_measuring.R: 2025-12-02 00:25:26.26 Preprocessing input data
    > test-selective_measuring.R: 2025-12-02 00:25:26.27 Mocking is enabled for 'preprocess_data'. Returning 'mockdata/RPCD_prepro.rds'.
    > test-selective_measuring.R: 2025-12-02 00:25:26.27 Standardizing features
    > test-selective_measuring.R: 2025-12-02 00:25:26.28 Training Ridge Regression model
    > test-selective_measuring.R: 2025-12-02 00:25:26.28 Fitting Ridge model
    > test-plot_frm.R: 2025-12-02 00:25:25.31 dim(new_data): 25 x 3
    > test-plot_frm.R: 2025-12-02 00:25:26.58 predictors: 1, 2, 3, 4, 5, 6
    > test-plot_frm.R: 2025-12-02 00:25:26.58 nfolds: 5
    > test-plot_frm.R: 2025-12-02 00:25:26.58 Preprocessing data
    > test-plot_frm.R: 2025-12-02 00:25:26.59 Formula: RT_ADJ ~ RT + I(RT^2) + I(RT^3) + log(RT) + exp(RT) + sqrt(RT)
    > test-plot_frm.R: 2025-12-02 00:25:26.59 Estimating performance of adjusted model in CV
    > test-selective_measuring.R: 2025-12-02 00:25:26.65 End training
    > test-selective_measuring.R: 2025-12-02 00:25:26.65 Scaling features by coefficients of Ridge Regression model
    > test-plot_frm.R: 2025-12-02 00:25:26.66 Fitting adjustment model on full new data set
    > test-plot_frm.R: 2025-12-02 00:25:26.66 Returning adjusted frm object
    > test-selective_measuring.R: 2025-12-02 00:25:26.67 Applying PAM clustering
    > test-selective_measuring.R: 2025-12-02 00:25:27.17 Returning clustering results
    [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-train_frm-gbtree.R:5:5'): train_frm works if `method == "GBTree"` ──
    <subscriptOutOfBoundsError/error/condition>
    Error in `FUN(X[[i]], ...)`: subscript out of bounds
    Backtrace:
        ▆
     1. └─FastRet::train_frm(...) at test-train_frm-gbtree.R:5:5
     2.   └─base::lapply(tmp, "[[", 2)
    ── Error ('test-fit_gbtree.R:8:5'): fit.gbtrees works as expected ──────────────
    Error in `begin_iteration:end_iteration`: argument of length 0
    Backtrace:
        ▆
     1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:8:5
     2.   └─FastRet:::fit_gbtree_grid(...)
     3.     └─xgboost::xgb.train(...)
    ── Error ('test-fit_gbtree.R:16:5'): fit.gbtrees works for data from reverse phase column ──
    Error in `begin_iteration:end_iteration`: argument of length 0
    Backtrace:
        ▆
     1. └─FastRet:::fit_gbtree(df, verbose = 0) at test-fit_gbtree.R:16:5
     2.   └─FastRet:::fit_gbtree_grid(...)
     3.     └─xgboost::xgb.train(...)
    
    [ FAIL 3 | WARN 5 | SKIP 0 | PASS 19 ]
    Error:
    ! Test failures.
    Execution halted

Package: forecastML
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘combine_forecasts.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘combine_forecasts.Rmd’
  
  --- re-building ‘custom_functions.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘custom_functions.Rmd’
  
  --- re-building ‘grouped_forecast.Rmd’ using rmarkdown
  [00:35:37] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:35:41] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:35:45] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:35:50] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:35:53] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:35:56] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:36:00] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:36:01] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  [00:36:02] WARNING: src/objective/regression_obj.cu:282: reg:linear is now deprecated in favor of reg:squarederror.
  
  Quitting from grouped_forecast.Rmd:320-322 [unnamed-chunk-14]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `array()`:
  ! length of 'dimnames' [1] not equal to array extent
  ---
  Backtrace:
      ▆
   1. ├─base::summary(model_results_cv$horizon_1$window_1$model)
   2. └─base::summary.default(model_results_cv$horizon_1$window_1$model)
   3.   └─base::array(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'grouped_forecast.Rmd' failed with diagnostics:
  length of 'dimnames' [1] not equal to array extent
  --- failed re-building ‘grouped_forecast.Rmd’
  
  --- re-building ‘lagged_features.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘lagged_features.Rmd’
  
  --- re-building ‘package_overview.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘package_overview.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘grouped_forecast.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: GeneralisedCovarianceMeasure
Check: R code for possible problems
New result: NOTE
  train.xgboost: no visible global function definition for
    ‘cb.evaluation.log’
  Undefined global functions or variables:
    cb.evaluation.log

Package: GPCERF
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘A-Note-on-Choosing-Hyperparameters.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘A-Note-on-Choosing-Hyperparameters.Rmd’
  
  --- re-building ‘Developers-Guide.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘Developers-Guide.Rmd’
  
  --- re-building ‘GPCERF.Rmd’ using rmarkdown
  2025-12-02 00:27:27.423109 anduin2 1085872 GPCERF estimate_gps INFO: Started estimating GPS values ... 
  
  Quitting from GPCERF.Rmd:38-45 [unnamed-chunk-4]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `SuperLearner::SuperLearner()`:
  ! All algorithms dropped from library
  ---
  Backtrace:
      ▆
   1. └─GPCERF::estimate_gps(...)
   2.   └─SuperLearner::SuperLearner(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'GPCERF.Rmd' failed with diagnostics:
  All algorithms dropped from library
  --- failed re-building ‘GPCERF.Rmd’
  
  --- re-building ‘Nearest-neighbor-Gaussian-Processes.Rmd’ using rmarkdown
  2025-12-02 00:27:34.973179 anduin2 1086318 GPCERF estimate_gps INFO: Started estimating GPS values ... 
  
  Quitting from Nearest-neighbor-Gaussian-Processes.Rmd:83-92 [unnamed-chunk-4]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `SuperLearner::SuperLearner()`:
  ! All algorithms dropped from library
  ---
  Backtrace:
      ▆
   1. └─GPCERF::estimate_gps(...)
   2.   └─SuperLearner::SuperLearner(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'Nearest-neighbor-Gaussian-Processes.Rmd' failed with diagnostics:
  All algorithms dropped from library
  --- failed re-building ‘Nearest-neighbor-Gaussian-Processes.Rmd’
  
  --- re-building ‘Standard-Gaussian-Processes.Rmd’ using rmarkdown
  2025-12-02 00:27:39.748312 anduin2 1087237 GPCERF estimate_gps INFO: Started estimating GPS values ... 
  
  Quitting from Standard-Gaussian-Processes.Rmd:83-93 [unnamed-chunk-3]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `SuperLearner::SuperLearner()`:
  ! All algorithms dropped from library
  ---
  Backtrace:
      ▆
   1. └─GPCERF::estimate_gps(...)
   2.   └─SuperLearner::SuperLearner(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'Standard-Gaussian-Processes.Rmd' failed with diagnostics:
  All algorithms dropped from library
  --- failed re-building ‘Standard-Gaussian-Processes.Rmd’
  
  SUMMARY: processing the following files failed:
    ‘GPCERF.Rmd’ ‘Nearest-neighbor-Gaussian-Processes.Rmd’
    ‘Standard-Gaussian-Processes.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: GPCERF
Check: tests
New result: ERROR
    Running ‘testthat.R’ [23s/22s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(GPCERF)
    > 
    > test_check("GPCERF")
    2025-12-02 00:27:01.411832 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Loading required package: nnls
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_deriv_nn-8.R
    2025-12-02 00:27:02.874024 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_deriv_weights_gp-8.R
    2025-12-02 00:27:03.802651 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_m_sigma-12.R
    2025-12-02 00:27:04.972649 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_posterior_m_nn-10.R
    2025-12-02 00:27:06.086513 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_posterior_sd_nn-10.R
    2025-12-02 00:27:06.952101 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_rl_deriv_gp-8.R
    2025-12-02 00:27:07.905843 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_rl_deriv_nn-8.R
    2025-12-02 00:27:08.960673 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-compute_sd_gp-17.R
    Saving _problems/test-compute_weight_gp-49.R
    2025-12-02 00:27:10.705621 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_cerf_gp-10.R
    2025-12-02 00:27:12.014942 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_cerf_nngp-10.R
    2025-12-02 00:27:13.146551 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_gps-10.R
    2025-12-02 00:27:14.338053 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_mean_sd_nn-10.R
    2025-12-02 00:27:15.591994 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_noise_gp-11.R
    2025-12-02 00:27:16.698685 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-estimate_noise_nn-10.R
    2025-12-02 00:27:17.845355 anduin2 1081295 GPCERF estimate_gps INFO: Started estimating GPS values ... 
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-find_optimal_nn-9.R
    [ FAIL 16 | WARN 752 | SKIP 0 | PASS 45 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-compute_deriv_nn.R:5:3'): compute_deriv_nn works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_deriv_nn.R:5:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_deriv_weights_gp.R:5:3'): compute_deriv_weights_gp works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_deriv_weights_gp.R:5:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_m_sigma.R:9:4'): compute_m_sigma works as expected! ────
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_m_sigma.R:9:4
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_posterior_m_nn.R:7:3'): compute_posterior_m_nn works as expected. ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_posterior_m_nn.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_posterior_sd_nn.R:7:3'): compute_posterior_sd_nn works as expected. ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_posterior_sd_nn.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_rl_deriv_gp.R:5:3'): compute_rl_deriv_gp works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_rl_deriv_gp.R:5:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_rl_deriv_nn.R:5:3'): compute_rl_deriv_nn works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_rl_deriv_nn.R:5:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-compute_sd_gp.R:14:4'): compute_sd_gp works as expected. ───────
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-compute_sd_gp.R:14:4
     2.   └─SuperLearner::SuperLearner(...)
    ── Failure ('test-compute_weight_gp.R:49:3'): multiplication works ─────────────
    Expected `weight$weight[28]` to equal 0.0002182767.
    Differences:
      `actual`: 0.00000
    `expected`: 0.00022
    
    ── Error ('test-estimate_cerf_gp.R:7:3'): estimate_cerf_gp works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-estimate_cerf_gp.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-estimate_cerf_nngp.R:7:3'): estimate_cerf_nngp works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-estimate_cerf_nngp.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-estimate_gps.R:7:3'): estimate_gps works as expected. ──────────
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-estimate_gps.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-estimate_mean_sd_nn.R:7:3'): estimate_mean_sd_nn works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-estimate_mean_sd_nn.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-estimate_noise_gp.R:8:3'): estimate_noise_gp works as expected ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-estimate_noise_gp.R:8:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-estimate_noise_nn.R:7:3'): estimate_noise_nn works as expected! ──
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-estimate_noise_nn.R:7:3
     2.   └─SuperLearner::SuperLearner(...)
    ── Error ('test-find_optimal_nn.R:6:3'): find_optimal_nn works as expected! ────
    Error in `SuperLearner::SuperLearner(Y = w_all, X = as.data.frame(cov_mt), SL.library = sl_lib)`: All algorithms dropped from library
    Backtrace:
        ▆
     1. └─GPCERF::estimate_gps(...) at test-find_optimal_nn.R:6:3
     2.   └─SuperLearner::SuperLearner(...)
    
    [ FAIL 16 | WARN 752 | SKIP 0 | PASS 45 ]
    Error:
    ! Test failures.
    Execution halted

Package: IBLM
Check: examples
New result: ERROR
  Running examples in ‘IBLM-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: beta_corrected_density
  > ### Title: Density Plot of Beta Corrections for a Variable
  > ### Aliases: beta_corrected_density
  > 
  > ### ** Examples
  > 
  > # This function is created inside explain_iblm() and is output as an item
  > 
  > df_list <- freMTPLmini |> split_into_train_validate_test(seed = 9000)
  > 
  > iblm_model <- train_iblm_xgb(
  +   df_list,
  +   response_var = "ClaimRate",
  +   family = "poisson"
  + )
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  > 
  > explain_objects <- explain_iblm(iblm_model, df_list$test)
  Error in xgboost::xgb.DMatrix(data.matrix(data)) : 
    [00:25:24] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
  Stack trace:
    [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f8a560798b1]
    [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f8a5627888d]
    [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f8a56531c46]
    [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f8a5606f59b]
    [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f8a6e703f6e]
    [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f8a6e740421]
    [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f8a6e75044b]
    [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f8a6e7507fb]
    [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f8a6e75275e]
  
  Calls: explain_iblm ... data.frame -> <Anonymous> -> predict.xgb.Booster -> <Anonymous>
  Execution halted

Package: IBLM
Check: tests
New result: ERROR
    Running ‘testthat.R’ [10s/8s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
    > # * https://testthat.r-lib.org/articles/special-files.html
    > 
    > library(testthat)
    > library(IBLM)
    > 
    > test_check("IBLM")
    Saving _problems/test-explain_iblm-222.R
    Saving _problems/test-explain_iblm-247.R
    Saving _problems/test-explain_iblm-272.R
    Saving _problems/test-explain_iblm-300.R
    Saving _problems/test-explain_iblm-328.R
    Saving _problems/test-explain_iblm-361.R
    Saving _problems/test-explain_iblm-421.R
    Saving _problems/test-explain_iblm-454.R
    Saving _problems/test-explain_iblm-484.R
    Saving _problems/test-predict-32.R
    [ FAIL 10 | WARN 12 | SKIP 4 | PASS 2 ]
    
    ══ Skipped tests (4) ═══════════════════════════════════════════════════════════
    • On CRAN (4): 'test-explain_iblm.R:4:3', 'test-get_pinball_scores.R:5:3',
      'test-get_pinball_scores.R:85:3', 'test-train_iblm.R:6:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Failure ('test-explain_iblm.R:214:3'): test explain completes when one categorical and one continuous ──
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:28] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Failure ('test-explain_iblm.R:239:3'): test explain completes when categorical only ──
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:29] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Failure ('test-explain_iblm.R:264:3'): test explain completes when continuous only ──
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:29] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Failure ('test-explain_iblm.R:292:3'): test explain completes when logical field ──
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:30] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Failure ('test-explain_iblm.R:320:3'): test explain completes when no reference/zero levels ──
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:30] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Error ('test-explain_iblm.R:361:3'): test migrate-to-bias vs non-migrate-to-bias options ──
    Error in `xgboost::xgb.DMatrix(data.matrix(data))`: [00:25:30] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
    Stack trace:
      [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
      [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
      [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
      [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
      [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
      [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
      [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
      [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
      [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
    
    
    Backtrace:
        ▆
     1. └─IBLM::explain_iblm(iblm_model = IBLM, data = splits$test, migrate_reference_to_bias = TRUE) at test-explain_iblm.R:361:3
     2.   ├─IBLM::extract_booster_shap(iblm_model$booster_model, data)
     3.   └─IBLM:::extract_booster_shap.xgb.Booster(...)
     4.     ├─base::data.frame(...)
     5.     ├─stats::predict(...)
     6.     ├─xgboost:::predict.xgb.Booster(...)
     7.     └─xgboost::xgb.DMatrix(data.matrix(data))
    ── Failure ('test-explain_iblm.R:413:3'): test gaussian can run ────────────────
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:30] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Failure ('test-explain_iblm.R:445:3'): test gamma can run ───────────────────
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:31] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Failure ('test-explain_iblm.R:475:3'): test tweedie can run ─────────────────
    Expected `{ ... }` not to throw any errors.
    Actually got a <simpleError> with message:
      [00:25:31] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
      Stack trace:
        [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
        [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
        [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
        [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
        [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
        [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
        [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
        [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
        [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
      
      
    ── Error ('test-predict.R:32:3'): test corrected beta coeffecient predictions are same as predict iblm() ──
    Error in `xgboost::xgb.DMatrix(data.matrix(data))`: [00:25:33] src/data/../collective/../data/array_interface.h:422: Check failed: ptr % alignment == 0 (1 vs. 0) : Input pointer misalignment.
    Stack trace:
      [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f3486a798b1]
      [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x27888d) [0x7f3486c7888d]
      [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromDense+0x146) [0x7f3486f31c46]
      [bt] (3) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixCreateFromMat_R+0x21b) [0x7f3486a6f59b]
      [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f349f303f6e]
      [bt] (5) /home/hornik/tmp/R/lib/libR.so(+0x140421) [0x7f349f340421]
      [bt] (6) /home/hornik/tmp/R/lib/libR.so(+0x15044b) [0x7f349f35044b]
      [bt] (7) /home/hornik/tmp/R/lib/libR.so(Rf_eval+0x14b) [0x7f349f3507fb]
      [bt] (8) /home/hornik/tmp/R/lib/libR.so(+0x15275e) [0x7f349f35275e]
    
    
    Backtrace:
        ▆
     1. └─IBLM::explain_iblm(iblm_model = IBLM, data = splits$test, migrate_reference_to_bias = TRUE) at test-predict.R:32:3
     2.   ├─IBLM::extract_booster_shap(iblm_model$booster_model, data)
     3.   └─IBLM:::extract_booster_shap.xgb.Booster(...)
     4.     ├─base::data.frame(...)
     5.     ├─stats::predict(...)
     6.     ├─xgboost:::predict.xgb.Booster(...)
     7.     └─xgboost::xgb.DMatrix(data.matrix(data))
    
    [ FAIL 10 | WARN 12 | SKIP 4 | PASS 2 ]
    Error:
    ! Test failures.
    Execution halted

Package: inTrees
Check: examples
New result: ERROR
  Running examples in ‘inTrees-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: XGB2List
  > ### Title: Transform an xgboost object to a list of trees
  > ### Aliases: XGB2List
  > ### Keywords: xgboost
  > 
  > ### ** Examples
  > 
  > 	library(data.table)
  > 	library(xgboost)
  > 	# test data set 1: iris
  > 	X <- within(iris,rm("Species")); Y <- iris[,"Species"]
  > 	X <- within(iris,rm("Species")); Y <- iris[,"Species"]
  > 	model_mat <- model.matrix(~. -1, data=X)
  > 	xgb <- xgboost(model_mat, label = as.numeric(Y) - 1, nrounds = 20, 
  + 		objective = "multi:softprob", num_class = 3 )
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: num_class. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Error in process.y.margin.and.objective(y, base_margin, objective, params) : 
    Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: multi:softprob
  Calls: xgboost -> process.y.margin.and.objective
  Execution halted

Package: IVDML
Check: tests
New result: ERROR
    Running ‘testthat.R’ [16s/17s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
    > # * https://testthat.r-lib.org/articles/special-files.html
    > 
    > library(testthat)
    > library(IVDML)
    > 
    > test_check("IVDML")
    Saving _problems/test-fit_IVDML-86.R
    Saving _problems/test-fit_IVDML-98.R
    Fitted IVDML object
    Machine learning method:  gam 
    IV methods:  linearIV, mlIV 
    Number of cross-fitting sample splits:  1[ FAIL 2 | WARN 6 | SKIP 0 | PASS 31 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Failure ('test-fit_IVDML.R:86:3'): fit_IVDML works when ml_par are set globally. ──
    Expected `fit_IVDML(...)` not to throw any errors.
    Actually got a <getvarError> with message:
      argument "y" is missing, with no default
    ── Failure ('test-fit_IVDML.R:98:3'): fit_IVDML works when ml_par is set differently for the different nuisance functions. ──
    Expected `fit_IVDML(...)` not to throw any errors.
    Actually got a <getvarError> with message:
      argument "y" is missing, with no default
    
    [ FAIL 2 | WARN 6 | SKIP 0 | PASS 31 ]
    Error:
    ! Test failures.
    Execution halted

Package: ldmppr
Check: examples
New result: ERROR
  Running examples in ‘ldmppr-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: predict_marks
  > ### Title: Predict values from the mark distribution
  > ### Aliases: predict_marks
  > 
  > ### ** Examples
  > 
  > # Simulate a realization
  > generating_parameters <- c(2, 8, .02, 2.5, 3, 1, 2.5, .2)
  > M_n <- matrix(c(10, 14), ncol = 1)
  > generated_locs <- simulate_sc(
  +   t_min = 0,
  +   t_max = 1,
  +   sc_params = generating_parameters,
  +   anchor_point = M_n,
  +   xy_bounds = c(0, 25, 0, 25)
  + )
  > 
  > # Load the raster files
  > raster_paths <- list.files(system.file("extdata", package = "ldmppr"),
  +   pattern = "\\.tif$", full.names = TRUE
  + )
  > rasters <- lapply(raster_paths, terra::rast)
  > 
  > # Scale the rasters
  > scaled_raster_list <- scale_rasters(rasters)
  > 
  > # Load the example mark model
  > file_path <- system.file("extdata", "example_mark_model.rds", package = "ldmppr")
  > example_mark_model <- readRDS(file_path)
  > 
  > # Unbundle the model
  > mark_model <- bundle::unbundle(example_mark_model)
  Error in xgboost::xgb.load.raw(object, as_booster = TRUE) : 
    unused argument (as_booster = TRUE)
  Calls: <Anonymous> ... <Anonymous> -> unbundle -> unbundle.bundle -> <Anonymous>
  Execution halted
  Examples with CPU (user + system) or elapsed time > 5s
                  user system elapsed
  extract_covars 6.481    0.3   6.782

Package: ldmppr
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘ldmppr_howto.Rmd’ using rmarkdown
  
  Quitting from ldmppr_howto.Rmd:100-130 [train_mark_model]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `xgboost::xgb.load.raw()`:
  ! unused argument (as_booster = TRUE)
  ---
  Backtrace:
      ▆
   1. ├─bundle::unbundle(example_mark_model$bundled_model)
   2. └─bundle:::unbundle.bundle(example_mark_model$bundled_model)
   3.   └─x$situate(get_object(x))
   4.     └─bundle::swap_element(object, "fit")
   5.       ├─bundle::unbundle(component)
   6.       └─bundle:::unbundle.bundle(component)
   7.         └─x$situate(get_object(x))
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'ldmppr_howto.Rmd' failed with diagnostics:
  unused argument (as_booster = TRUE)
  --- failed re-building ‘ldmppr_howto.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘ldmppr_howto.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: lime
Check: tests
New result: ERROR
    Running ‘testthat.R’ [28s/28s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(lime)
    > 
    > test_check("lime")
    Saving _problems/test-text-28.R
    [ FAIL 1 | WARN 6 | SKIP 1 | PASS 21 ]
    
    ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
    • On CRAN (1): 'test-h2o.R:3:1'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Failure ('test-text.R:28:3'): single sentence explanation ───────────────────
    Expected `explanation` to have length 13.
    Actual length: 11.
    
    [ FAIL 1 | WARN 6 | SKIP 1 | PASS 21 ]
    Error:
    ! Test failures.
    Execution halted

Package: MBMethPred
Check: examples
New result: ERROR
  Running examples in ‘MBMethPred-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: ModelMetrics
  > ### Title: Model metrics
  > ### Aliases: ModelMetrics
  > 
  > ### ** Examples
  > 
  > xgboost <- XGBoostModel(SplitRatio = 0.2,
  +                         CV = 2,
  +                         NCores = 1,
  +                         NewData = NULL)
  Warning in throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
    Parameter(s) have been removed from this function: params. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose, num_class. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Error in prescreen.objective(objective) : 
    Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
  Calls: XGBoostModel ... lapply -> FUN -> <Anonymous> -> prescreen.objective
  Execution halted

Package: MBMethPred
Check: tests
New result: ERROR
    Running ‘testthat.R’ [18s/18s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(MBMethPred)
    > 
    > test_check("MBMethPred")
            y_pred
    y_true   Group3 SHH Group4
      Group3      1   0      0
      SHH         1   1      0
      Group4      0   0      1
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3      8   0   0      0
      SHH         0  24   2      0
      WNT         0   0  24      0
      Group4      1   0   0     31
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     20   0   0      1
      SHH         0  20   0      0
      WNT         0   0  18      0
      Group4      1   0   0     32
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     18   0   0      1
      SHH         0  41   0      0
      WNT         0   0  37      0
      Group4      6   2   0     52
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     26   0   1      0
      SHH         0  41   0      0
      WNT         0   0  38      0
      Group4      1   1   0     53
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     27   0   0      0
      SHH         0  43   0      0
      WNT         0   0  37      0
      Group4      4   2   0     54
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     23   0   0      3
      SHH         0  42   0      0
      WNT         0   0  35      0
      Group4      6   1   0     55
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     26   0   0      1
      SHH         0  38   1      0
      WNT         0   0  39      0
      Group4      5   0   0     52
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     26   0   0      2
      SHH         0  41   0      0
      WNT         0   0  39      0
      Group4      0   0   0     57
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     27   0   0      2
      SHH         0  41   0      0
      WNT         0   0  39      0
      Group4      3   1   1     53
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     21   0   0      3
      SHH         0  42   0      0
      WNT         0   0  37      0
      Group4      6   0   0     54
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     20   0   1      5
      SHH         0  42   0      0
      WNT         0   0  37      0
      Group4      6   1   0     46
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     27   0   0      1
      SHH         0  41   1      0
      WNT         0   0  40      0
      Group4      6   0   0     57
    
    Saving _problems/test-ModelMetrics-2.R
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     13   0   0      0
      SHH         0  27   0      0
      WNT         0   0  22      1
      Group4      1   0   0     24
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     15   0   0      1
      SHH         0  19   0      0
      WNT         0   0  19      0
      Group4      4   0   0     36
    
    Saving _problems/test-NewDataPredictionResult-10.R
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     15   0   0      2
      SHH         0  18   0      0
      WNT         0   0  18      0
      Group4      0   0   0     34
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     11   0   0      1
      SHH         0  27   0      1
      WNT         0   0  24      0
      Group4      0   0   0     31
    
    The NMI score is: 0.903790812437385
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     12   0   0      1
      SHH         0  26   0      0
      WNT         1   0  20      0
      Group4      0   0   0     31
    
            y_pred
    y_true   Group3 SHH WNT Group4
      Group3     15   0   0      1
      SHH         0  20   0      0
      WNT         0   0  21      0
      Group4      2   0   0     32
    
    Saving _problems/test-XGBoostModel-2.R
    [ FAIL 3 | WARN 12 | SKIP 0 | PASS 9 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-ModelMetrics.r:2:3'): ModelMetrics returns correct class. ──────
    Error in `prescreen.objective(objective)`: Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
    Backtrace:
        ▆
     1. └─MBMethPred::XGBoostModel(...) at test-ModelMetrics.r:2:3
     2.   └─parallel::mclapply(...)
     3.     └─base::lapply(X = X, FUN = FUN, ...)
     4.       └─MBMethPred (local) FUN(X[[i]], ...)
     5.         └─xgboost::xgboost(...)
     6.           └─xgboost:::prescreen.objective(objective)
    ── Error ('test-NewDataPredictionResult.R:10:3'): NewDataPredictionResult returns correct type ──
    Error in `prescreen.objective(objective)`: Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
    Backtrace:
        ▆
     1. └─MBMethPred::XGBoostModel(...) at test-NewDataPredictionResult.R:10:3
     2.   └─parallel::mclapply(...)
     3.     └─base::lapply(X = X, FUN = FUN, ...)
     4.       └─MBMethPred (local) FUN(X[[i]], ...)
     5.         └─xgboost::xgboost(...)
     6.           └─xgboost:::prescreen.objective(objective)
    ── Error ('test-XGBoostModel.R:2:3'): XGBoostModel returns correct class. ──────
    Error in `prescreen.objective(objective)`: Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
    Backtrace:
        ▆
     1. ├─testthat::expect_type(...) at test-XGBoostModel.R:2:3
     2. │ └─testthat::quasi_label(enquo(object))
     3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
     4. └─MBMethPred::XGBoostModel(...)
     5.   └─parallel::mclapply(...)
     6.     └─base::lapply(X = X, FUN = FUN, ...)
     7.       └─MBMethPred (local) FUN(X[[i]], ...)
     8.         └─xgboost::xgboost(...)
     9.           └─xgboost:::prescreen.objective(objective)
    
    [ FAIL 3 | WARN 12 | SKIP 0 | PASS 9 ]
    Error:
    ! Test failures.
    Execution halted

Package: MIC
Check: dependencies in R code
New result: WARNING
  Missing or unexported object: ‘xgboost::slice’

Package: MIC
Check: examples
New result: ERROR
  Running examples in ‘MIC-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: xgb.cv.lowmem
  > ### Title: Low memory cross-validation wrapper for XGBoost
  > ### Aliases: xgb.cv.lowmem
  > 
  > ### ** Examples
  > 
  > train <- list(data = matrix(rnorm(20), ncol = 2),
  +              label = rbinom(10, 1, 0.5))
  > dtrain <- xgboost::xgb.DMatrix(train$data, label = train$label, nthread = 1)
  > cv <- xgb.cv.lowmem(data = dtrain,
  +                    params = list(objective = "binary:logistic"),
  +                    nrounds = 2,
  +                    nfold = 3,
  +                    prediction = TRUE,
  +                    nthread = 1)
  Warning: `xgb.cv.lowmem()` was deprecated in MIC 1.2.0.
  ℹ Please use `faLearn::xgb.cv.lowmem()` instead.
  ℹ This function has been moved to the faLearn package.
  Error: 'slice' is not an exported object from 'namespace:xgboost'
  Execution halted

Package: MIC
Check: tests
New result: ERROR
    Running ‘testthat.R’ [86s/86s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/tests.html
    > # * https://testthat.r-lib.org/reference/test_package.html#special-files
    > 
    > library(testthat)
    > library(MIC)
    
    Attaching package: 'MIC'
    
    The following object is masked from 'package:base':
    
        table
    
    > 
    > test_check("MIC")
    Saving _problems/test-cv-148.R
    [ FAIL 1 | WARN 420 | SKIP 4 | PASS 231 ]
    
    ══ Skipped tests (4) ═══════════════════════════════════════════════════════════
    • On CRAN (4): 'test-cv.R:4:3', 'test-patric.R:17:3', 'test-patric.R:32:3',
      'test-patric.R:39:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-cv.R:148:3'): iris example ─────────────────────────────────────
    Error in `apply(model$pred, 1, which.max)`: dim(X) must have a positive length
    Backtrace:
        ▆
     1. └─base::apply(model$pred, 1, which.max) at test-cv.R:148:3
    
    [ FAIL 1 | WARN 420 | SKIP 4 | PASS 231 ]
    Error:
    ! Test failures.
    Execution halted

Package: mixgb
Check: examples
New result: ERROR
  Running examples in ‘mixgb-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: mixgb_cv
  > ### Title: Use cross-validation to find the optimal 'nrounds'
  > ### Aliases: mixgb_cv
  > 
  > ### ** Examples
  > 
  > params <- list(max_depth = 3, subsample = 0.7, nthread = 2)
  > cv.results <- mixgb_cv(data = nhanes3, xgb.params = params)
  Warning in throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
    Parameter(s) have been removed from this function: label. This warning will become an error in a future version.
  Error in xgb.cv(data = obs.data, params = xgb.params, label = obs.y, objective = obj.type,  : 
    inherits(data, "xgb.DMatrix") is not TRUE
  Calls: mixgb_cv -> xgb.cv -> stopifnot
  Execution halted

Package: mixgb
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘Imputing-newdata.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘Imputing-newdata.Rmd’
  
  --- re-building ‘Using-mixgb.Rmd’ using rmarkdown
  
  Quitting from Using-mixgb.Rmd:114-120 [unnamed-chunk-5]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `xgb.cv()`:
  ! inherits(data, "xgb.DMatrix") is not TRUE
  ---
  Backtrace:
      ▆
   1. └─mixgb::mixgb_cv(...)
   2.   └─xgboost::xgb.cv(...)
   3.     └─base::stopifnot(inherits(data, "xgb.DMatrix"))
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'Using-mixgb.Rmd' failed with diagnostics:
  inherits(data, "xgb.DMatrix") is not TRUE
  --- failed re-building ‘Using-mixgb.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘Using-mixgb.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: mllrnrs
Check: tests
New result: ERROR
    Running ‘testthat.R’ [43s/99s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/tests.html
    > # * https://testthat.r-lib.org/reference/test_package.html#special-files
    > # https://github.com/Rdatatable/data.table/issues/5658
    > Sys.setenv("OMP_THREAD_LIMIT" = 2)
    > Sys.setenv("Ncpu" = 2)
    > 
    > library(testthat)
    > library(mllrnrs)
    > 
    > test_check("mllrnrs")
    
    CV fold: Fold1
    
    CV fold: Fold1
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  4.824 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        7.199 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.348 seconds
    
    CV fold: Fold2
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  4.906 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        6.172 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.369 seconds
    
    CV fold: Fold3
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  4.825 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        10.453 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.379 seconds
    
    CV fold: Fold1
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
    CV fold: Fold2
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
    CV fold: Fold3
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
    CV fold: Fold1
    Saving _problems/test-binary-356.R
    
    CV fold: Fold1
    
    CV fold: Fold2
    
    CV fold: Fold3
    
    CV fold: Fold1
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
    CV fold: Fold2
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
    CV fold: Fold3
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
     Classification: using 'mean classification error' as optimization metric.
    
    CV fold: Fold1
    Saving _problems/test-multiclass-294.R
    
    CV fold: Fold1
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 5 times in 2 thread(s)...  4.509 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.964 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.324 seconds
    
    CV fold: Fold2
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 5 times in 2 thread(s)...  4.338 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        1.385 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.674 seconds
    
    CV fold: Fold3
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 5 times in 2 thread(s)...  4.271 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        1.308 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.454 seconds
    
    CV fold: Fold1
    
    CV fold: Fold2
    
    CV fold: Fold3
    
    CV fold: Fold1
    
     Regression: using 'mean squared error' as optimization metric.
    
     Regression: using 'mean squared error' as optimization metric.
    
     Regression: using 'mean squared error' as optimization metric.
    
    CV fold: Fold2
    
     Regression: using 'mean squared error' as optimization metric.
    
     Regression: using 'mean squared error' as optimization metric.
    
     Regression: using 'mean squared error' as optimization metric.
    
    CV fold: Fold3
    
     Regression: using 'mean squared error' as optimization metric.
    
     Regression: using 'mean squared error' as optimization metric.
    
     Regression: using 'mean squared error' as optimization metric.
    
    CV fold: Fold1
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  5.474 seconds
        subsample colsample_bytree min_child_weight learning_rate max_depth
            <num>            <num>            <num>         <num>     <num>
     1:       1.0              0.8                1           0.1         5
     2:       0.8              1.0                1           0.2         5
     3:       1.0              1.0                5           0.2         5
     4:       0.6              0.8                1           0.1         5
     5:       0.6              0.8                5           0.2         5
     6:       0.8              0.8                5           0.2         5
     7:       0.8              0.8                1           0.1         1
     8:       0.6              0.6                1           0.2         5
     9:       0.6              1.0                1           0.1         1
    10:       0.6              0.8                1           0.2         5
                                                                errorMessage
                                                                      <char>
     1: FUN returned these elements with length > 1: Score,metric_optim_mean
     2: FUN returned these elements with length > 1: Score,metric_optim_mean
     3: FUN returned these elements with length > 1: Score,metric_optim_mean
     4: FUN returned these elements with length > 1: Score,metric_optim_mean
     5: FUN returned these elements with length > 1: Score,metric_optim_mean
     6: FUN returned these elements with length > 1: Score,metric_optim_mean
     7: FUN returned these elements with length > 1: Score,metric_optim_mean
     8: FUN returned these elements with length > 1: Score,metric_optim_mean
     9: FUN returned these elements with length > 1: Score,metric_optim_mean
    10: FUN returned these elements with length > 1: Score,metric_optim_mean
    Saving _problems/test-regression-309.R
    
    CV fold: Fold1
    Saving _problems/test-regression-352.R
    [ FAIL 4 | WARN 3 | SKIP 3 | PASS 22 ]
    
    ══ Skipped tests (3) ═══════════════════════════════════════════════════════════
    • On CRAN (3): 'test-binary.R:57:5', 'test-lints.R:10:5',
      'test-multiclass.R:57:5'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-binary.R:356:5'): test nested cv, grid, binary:logistic - xgboost ──
    Error in `.get_best_setting(results = outlist$summary, opt_metric = "metric_optim_mean", param_names = param_names, higher_better = metric_higher_better)`: nrow(best_row) == 1 is not TRUE
    Backtrace:
         ▆
      1. └─xgboost_optimizer$execute() at test-binary.R:356:5
      2.   └─mlexperiments:::.run_cv(self = self, private = private)
      3.     └─mlexperiments:::.fold_looper(self, private)
      4.       ├─base::do.call(private$cv_run_model, run_args)
      5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
      6.         ├─base::do.call(.cv_run_nested_model, args)
      7.         └─mlexperiments (local) `<fn>`(...)
      8.           └─hparam_tuner$execute(k = self$k_tuning)
      9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
     10.               └─mlexperiments:::.run_optimizer(...)
     11.                 └─mlexperiments:::.optimize_postprocessing(...)
     12.                   └─mlexperiments:::.get_best_setting(...)
     13.                     └─base::stopifnot(nrow(best_row) == 1)
    ── Error ('test-multiclass.R:294:5'): test nested cv, grid, multi:softprob - xgboost, with weights ──
    Error in `.get_best_setting(results = outlist$summary, opt_metric = "metric_optim_mean", param_names = param_names, higher_better = metric_higher_better)`: nrow(best_row) == 1 is not TRUE
    Backtrace:
         ▆
      1. └─xgboost_optimizer$execute() at test-multiclass.R:294:5
      2.   └─mlexperiments:::.run_cv(self = self, private = private)
      3.     └─mlexperiments:::.fold_looper(self, private)
      4.       ├─base::do.call(private$cv_run_model, run_args)
      5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
      6.         ├─base::do.call(.cv_run_nested_model, args)
      7.         └─mlexperiments (local) `<fn>`(...)
      8.           └─hparam_tuner$execute(k = self$k_tuning)
      9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
     10.               └─mlexperiments:::.run_optimizer(...)
     11.                 └─mlexperiments:::.optimize_postprocessing(...)
     12.                   └─mlexperiments:::.get_best_setting(...)
     13.                     └─base::stopifnot(nrow(best_row) == 1)
    ── Error ('test-regression.R:309:5'): test nested cv, bayesian, reg:squarederror - xgboost ──
    Error in `(function (FUN, bounds, saveFile = NULL, initGrid, initPoints = 4, iters.n = 3, iters.k = 1, otherHalting = list(timeLimit = Inf, minUtility = 0), acq = "ucb", kappa = 2.576, eps = 0, parallel = FALSE, gsPoints = pmax(100, length(bounds)^3), convThresh = 1e+08, acqThresh = 1, errorHandling = "stop", plotProgress = FALSE, verbose = 1, ...)  {     startT <- Sys.time()     optObj <- list()     class(optObj) <- "bayesOpt"     optObj$FUN <- FUN     optObj$bounds <- bounds     optObj$iters <- 0     optObj$initPars <- list()     optObj$optPars <- list()     optObj$GauProList <- list()     optObj <- changeSaveFile(optObj, saveFile)     checkParameters(bounds, iters.n, iters.k, otherHalting, acq, acqThresh, errorHandling, plotProgress, parallel, verbose)     boundsDT <- boundsToDT(bounds)     otherHalting <- formatOtherHalting(otherHalting)     if (missing(initGrid) + missing(initPoints) != 1)          stop("Please provide 1 of initGrid or initPoints, but not both.")     if (!missing(initGrid)) {         setDT(initGrid)         inBounds <- checkBounds(initGrid, bounds)         inBounds <- as.logical(apply(inBounds, 1, prod))         if (any(!inBounds))              stop("initGrid not within bounds.")         optObj$initPars$initialSample <- "User Provided Grid"         initPoints <- nrow(initGrid)     }     else {         initGrid <- randParams(boundsDT, initPoints)         optObj$initPars$initialSample <- "Latin Hypercube Sampling"     }     optObj$initPars$initGrid <- initGrid     if (nrow(initGrid) <= 2)          stop("Cannot initialize with less than 3 samples.")     optObj$initPars$initPoints <- nrow(initGrid)     if (initPoints <= length(bounds))          stop("initPoints must be greater than the number of FUN inputs.")     sinkFile <- file()     on.exit({         while (sink.number() > 0) sink()         close(sinkFile)     })     `%op%` <- ParMethod(parallel)     if (parallel)          Workers <- getDoParWorkers()     else Workers <- 1     if (verbose > 0)          cat("\nRunning initial scoring function", nrow(initGrid), "times in", Workers, "thread(s)...")     sink(file = sinkFile)     tm <- system.time(scoreSummary <- foreach(iter = 1:nrow(initGrid), .options.multicore = list(preschedule = FALSE), .combine = list, .multicombine = TRUE, .inorder = FALSE, .errorhandling = "pass", .verbose = FALSE) %op% {         Params <- initGrid[get("iter"), ]         Elapsed <- system.time(Result <- tryCatch({             do.call(what = FUN, args = as.list(Params))         }, error = function(e) e))         if (any(class(Result) %in% c("simpleError", "error", "condition")))              return(Result)         if (!inherits(x = Result, what = "list"))              stop("Object returned from FUN was not a list.")         resLengths <- lengths(Result)         if (!any(names(Result) == "Score"))              stop("FUN must return list with element 'Score' at a minimum.")         if (!is.numeric(Result$Score))              stop("Score returned from FUN was not numeric.")         if (any(resLengths != 1)) {             badReturns <- names(Result)[which(resLengths != 1)]             stop("FUN returned these elements with length > 1: ", paste(badReturns, collapse = ","))         }         data.table(Params, Elapsed = Elapsed[[3]], as.data.table(Result))     })[[3]]     while (sink.number() > 0) sink()     if (verbose > 0)          cat(" ", tm, "seconds\n")     se <- which(sapply(scoreSummary, function(cl) any(class(cl) %in% c("simpleError", "error", "condition"))))     if (length(se) > 0) {         print(data.table(initGrid[se, ], errorMessage = sapply(scoreSummary[se], function(x) x$message)))         stop("Errors encountered in initialization are listed above.")     }     else {         scoreSummary <- rbindlist(scoreSummary)     }     scoreSummary[, `:=`(("gpUtility"), rep(as.numeric(NA), nrow(scoreSummary)))]     scoreSummary[, `:=`(("acqOptimum"), rep(FALSE, nrow(scoreSummary)))]     scoreSummary[, `:=`(("Epoch"), rep(0, nrow(scoreSummary)))]     scoreSummary[, `:=`(("Iteration"), 1:nrow(scoreSummary))]     scoreSummary[, `:=`(("inBounds"), rep(TRUE, nrow(scoreSummary)))]     scoreSummary[, `:=`(("errorMessage"), rep(NA, nrow(scoreSummary)))]     extraRet <- setdiff(names(scoreSummary), c("Epoch", "Iteration", boundsDT$N, "inBounds", "Elapsed", "Score", "gpUtility", "acqOptimum"))     setcolorder(scoreSummary, c("Epoch", "Iteration", boundsDT$N, "gpUtility", "acqOptimum", "inBounds", "Elapsed", "Score", extraRet))     if (any(scoreSummary$Elapsed < 1) & acq == "eips") {         cat("\n   FUN elapsed time is too low to be precise. Switching acq to 'ei'.\n")         acq <- "ei"     }     optObj$optPars$acq <- acq     optObj$optPars$kappa <- kappa     optObj$optPars$eps <- eps     optObj$optPars$parallel <- parallel     optObj$optPars$gsPoints <- gsPoints     optObj$optPars$convThresh <- convThresh     optObj$optPars$acqThresh <- acqThresh     optObj$scoreSummary <- scoreSummary     optObj$GauProList$gpUpToDate <- FALSE     optObj$iters <- nrow(scoreSummary)     optObj$stopStatus <- "OK"     optObj$elapsedTime <- as.numeric(difftime(Sys.time(), startT, units = "secs"))     saveSoFar(optObj, 0)     optObj <- addIterations(optObj, otherHalting = otherHalting, iters.n = iters.n, iters.k = iters.k, parallel = parallel, plotProgress = plotProgress, errorHandling = errorHandling, saveFile = saveFile, verbose = verbose, ...)     return(optObj) })(FUN = function (...)  {     kwargs <- list(...)     args <- .method_params_refactor(kwargs, method_helper)     set.seed(self$seed)     res <- do.call(private$fun_bayesian_scoring_function, args)     if (isFALSE(self$metric_optimization_higher_better)) {         res$Score <- as.numeric(I(res$Score * -1L))     }     return(res) }, bounds = list(subsample = c(0.2, 1), colsample_bytree = c(0.2, 1), min_child_weight = c(1L, 10L), learning_rate = c(0.1, 0.2), max_depth = c(1L, 10L)), initGrid = structure(list(subsample = c(1, 0.8, 1, 0.6, 0.6, 0.8, 0.8, 0.6, 0.6, 0.6), colsample_bytree = c(0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.6, 1, 0.8), min_child_weight = c(1, 1, 5, 1, 5, 5, 1, 1, 1, 1), learning_rate = c(0.1, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.2, 0.1, 0.2), max_depth = c(5, 5, 5, 5, 5, 5, 1, 5, 1, 5)), out.attrs = list(dim = c(subsample = 3L,  colsample_bytree = 3L, min_child_weight = 2L, learning_rate = 2L, max_depth = 2L), dimnames = list(subsample = c("subsample=0.6", "subsample=0.8", "subsample=1.0"), colsample_bytree = c("colsample_bytree=0.6", "colsample_bytree=0.8", "colsample_bytree=1.0"), min_child_weight = c("min_child_weight=1", "min_child_weight=5"), learning_rate = c("learning_rate=0.1", "learning_rate=0.2"), max_depth = c("max_depth=1", "max_depth=5"))), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55a85087dfc0>),      iters.n = 2L, iters.k = 2L, otherHalting = list(timeLimit = Inf, minUtility = 0), acq = "ucb", kappa = 3.5, eps = 0, parallel = TRUE, gsPoints = 125, convThresh = 1e+08, acqThresh = 1, errorHandling = "stop", plotProgress = FALSE, verbose = 1)`: Errors encountered in initialization are listed above.
    Backtrace:
         ▆
      1. └─xgboost_optimizer$execute() at test-regression.R:309:5
      2.   └─mlexperiments:::.run_cv(self = self, private = private)
      3.     └─mlexperiments:::.fold_looper(self, private)
      4.       ├─base::do.call(private$cv_run_model, run_args)
      5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
      6.         ├─base::do.call(.cv_run_nested_model, args)
      7.         └─mlexperiments (local) `<fn>`(...)
      8.           └─hparam_tuner$execute(k = self$k_tuning)
      9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
     10.               └─mlexperiments:::.run_optimizer(...)
     11.                 └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper)
     12.                   ├─base::do.call(...)
     13.                   └─mlexperiments (local) `<fn>`(...)
     14.                     ├─base::do.call(ParBayesianOptimization::bayesOpt, args)
     15.                     └─ParBayesianOptimization (local) `<fn>`(...)
    ── Error ('test-regression.R:352:5'): test nested cv, grid - xgboost ───────────
    Error in `.get_best_setting(results = outlist$summary, opt_metric = "metric_optim_mean", param_names = param_names, higher_better = metric_higher_better)`: nrow(best_row) == 1 is not TRUE
    Backtrace:
         ▆
      1. └─xgboost_optimizer$execute() at test-regression.R:352:5
      2.   └─mlexperiments:::.run_cv(self = self, private = private)
      3.     └─mlexperiments:::.fold_looper(self, private)
      4.       ├─base::do.call(private$cv_run_model, run_args)
      5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
      6.         ├─base::do.call(.cv_run_nested_model, args)
      7.         └─mlexperiments (local) `<fn>`(...)
      8.           └─hparam_tuner$execute(k = self$k_tuning)
      9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
     10.               └─mlexperiments:::.run_optimizer(...)
     11.                 └─mlexperiments:::.optimize_postprocessing(...)
     12.                   └─mlexperiments:::.get_best_setting(...)
     13.                     └─base::stopifnot(nrow(best_row) == 1)
    
    [ FAIL 4 | WARN 3 | SKIP 3 | PASS 22 ]
    Error:
    ! Test failures.
    Execution halted

Package: mlr3benchmark
Check: examples
New result: ERROR
  Running examples in ‘mlr3benchmark-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: autoplot.BenchmarkAggr
  > ### Title: Plots for BenchmarkAggr
  > ### Aliases: autoplot.BenchmarkAggr
  > 
  > ### ** Examples
  > 
  > if (requireNamespaces(c("mlr3learners", "mlr3", "rpart", "xgboost"))) {
  + library(mlr3)
  + library(mlr3learners)
  + library(ggplot2)
  + 
  + set.seed(1)
  + task = tsks(c("iris", "sonar", "wine", "zoo"))
  + learns = lrns(c("classif.featureless", "classif.rpart", "classif.xgboost"))
  + learns$classif.xgboost$param_set$values$nrounds = 50
  + bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 3)))
  + obj = as_benchmark_aggr(bm)
  + 
  + # mean and error bars
  + autoplot(obj, type = "mean", level = 0.95)
  + 
  + if (requireNamespace("PMCMRplus", quietly = TRUE)) {
  +   # critical differences
  +   autoplot(obj, type = "cd",style = 1)
  +   autoplot(obj, type = "cd",style = 2)
  + 
  +   # post-hoc friedman-nemenyi
  +   autoplot(obj, type = "fn")
  + }
  + 
  + }
  INFO  [00:34:19.431] [mlr3] Running benchmark with 36 resampling iterations
  INFO  [00:34:19.452] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 1/3)
  INFO  [00:34:19.484] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 2/3)
  INFO  [00:34:19.515] [mlr3] Applying learner 'classif.featureless' on task 'iris' (iter 3/3)
  INFO  [00:34:19.546] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 1/3)
  INFO  [00:34:19.582] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 2/3)
  INFO  [00:34:19.619] [mlr3] Applying learner 'classif.rpart' on task 'iris' (iter 3/3)
  INFO  [00:34:19.657] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 1/3)
  INFO  [00:34:19.724] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 2/3)
  INFO  [00:34:19.781] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 3/3)
  INFO  [00:34:19.833] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 1/3)
  INFO  [00:34:19.865] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 2/3)
  INFO  [00:34:19.897] [mlr3] Applying learner 'classif.featureless' on task 'sonar' (iter 3/3)
  INFO  [00:34:19.929] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 1/3)
  INFO  [00:34:19.992] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 2/3)
  INFO  [00:34:20.060] [mlr3] Applying learner 'classif.rpart' on task 'sonar' (iter 3/3)
  INFO  [00:34:20.124] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 1/3)
  INFO  [00:34:20.222] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 2/3)
  INFO  [00:34:20.310] [mlr3] Applying learner 'classif.xgboost' on task 'sonar' (iter 3/3)
  INFO  [00:34:20.400] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 1/3)
  INFO  [00:34:20.429] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 2/3)
  INFO  [00:34:20.454] [mlr3] Applying learner 'classif.featureless' on task 'wine' (iter 3/3)
  INFO  [00:34:20.480] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 1/3)
  INFO  [00:34:20.512] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 2/3)
  INFO  [00:34:20.540] [mlr3] Applying learner 'classif.rpart' on task 'wine' (iter 3/3)
  INFO  [00:34:20.569] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 1/3)
  INFO  [00:34:20.612] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 2/3)
  INFO  [00:34:20.654] [mlr3] Applying learner 'classif.xgboost' on task 'wine' (iter 3/3)
  INFO  [00:34:20.695] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 1/3)
  INFO  [00:34:20.716] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 2/3)
  INFO  [00:34:20.739] [mlr3] Applying learner 'classif.featureless' on task 'zoo' (iter 3/3)
  INFO  [00:34:20.759] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 1/3)
  INFO  [00:34:20.786] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 2/3)
  INFO  [00:34:20.812] [mlr3] Applying learner 'classif.rpart' on task 'zoo' (iter 3/3)
  INFO  [00:34:20.839] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 1/3)
  INFO  [00:34:20.932] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 2/3)
  INFO  [00:34:20.993] [mlr3] Applying learner 'classif.xgboost' on task 'zoo' (iter 3/3)
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: nthread, num_class, eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  INFO  [00:34:21.066] [mlr3] Finished benchmark
  Error: Global Friedman test non-significant (p > 0.05), try type = 'mean' instead.
  Execution halted

Package: mlr3tuning
Check: examples
New result: ERROR
  Running examples in ‘mlr3tuning-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: mlr_tuners_internal
  > ### Title: Hyperparameter Tuning with Internal Tuning
  > ### Aliases: mlr_tuners_internal TunerBatchInternal
  > 
  > ### ** Examples
  > 
  > ## Don't show: 
  > if (mlr3misc::require_namespaces(c("mlr3learners", "xgboost"), quietly = TRUE)) withAutoprint({ # examplesIf
  + ## End(Don't show)
  + library(mlr3learners)
  + 
  + # Retrieve task
  + task = tsk("pima")
  + 
  + # Load learner and set search space
  + learner = lrn("classif.xgboost",
  +   nrounds = to_tune(upper = 1000, internal = TRUE),
  +   early_stopping_rounds = 10,
  +   validate = "test",
  +   eval_metric = "merror"
  + )
  + 
  + # Internal hyperparameter tuning on the pima indians diabetes data set
  + instance = tune(
  +   tnr("internal"),
  +   tsk("iris"),
  +   learner,
  +   rsmp("cv", folds = 3),
  +   msr("internal_valid_score", minimize = TRUE, select = "merror")
  + )
  + 
  + # best performing hyperparameter configuration
  + instance$result_learner_param_vals
  + 
  + instance$result_learner_param_vals$internal_tuned_values
  + ## Don't show: 
  + }) # examplesIf
  > library(mlr3learners)
  > task = tsk("pima")
  > learner = lrn("classif.xgboost", nrounds = to_tune(upper = 1000, internal = TRUE), 
  +     early_stopping_rounds = 10, validate = "test", eval_metric = "merror")
  > instance = tune(tnr("internal"), tsk("iris"), learner, rsmp("cv", folds = 3), 
  +     msr("internal_valid_score", minimize = TRUE, select = "merror"))
  INFO  [00:40:31.408] [bbotk] Starting to optimize 0 parameter(s) with '<TunerBatchInternal>' and '<TerminatorNone>'
  INFO  [00:40:31.409] [bbotk] Evaluating 1 configuration(s)
  INFO  [00:40:31.417] [mlr3] Running benchmark with 3 resampling iterations
  INFO  [00:40:31.433] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 1/3)
  INFO  [00:40:31.487] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 2/3)
  INFO  [00:40:31.525] [mlr3] Applying learner 'classif.xgboost' on task 'iris' (iter 3/3)
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric, nthread, num_class. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric, nthread, num_class. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric, nthread, num_class. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning: Caught simpleError. Canceling all iterations ...
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric, nthread, num_class. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric, nthread, num_class. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  Warning in check.custom.obj(params, objective) :
    Argument 'objective' is only for custom objectives. For built-in objectives, pass the objective under 'params'. This warning will become an error in a future version.
  Error in names(x) <- nm : attempt to set an attribute on NULL
  Calls: withAutoprint ... tryCatchList -> tryCatchOne -> <Anonymous> -> onError
  Execution halted

Package: mlspatial
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘mlspatial.Rmd’ using rmarkdown
  
  Quitting from mlspatial.Rmd:127-181 [unnamed-chunk-7]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `xgboost()`:
  ! argument "y" is missing, with no default
  ---
  Backtrace:
      ▆
   1. └─xgboost::xgboost(...)
   2.   └─xgboost:::process.y.margin.and.objective(...)
   3.     └─base::NROW(y)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'mlspatial.Rmd' failed with diagnostics:
  argument "y" is missing, with no default
  --- failed re-building ‘mlspatial.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘mlspatial.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: mlsurvlrnrs
Check: tests
New result: ERROR
    Running ‘testthat.R’ [19s/87s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/tests.html
    > # * https://testthat.r-lib.org/reference/test_package.html#special-files
    > 
    > Sys.setenv("OMP_THREAD_LIMIT" = 2)
    > Sys.setenv("Ncpu" = 2)
    > 
    > library(testthat)
    > library(mlsurvlrnrs)
    > 
    > test_check("mlsurvlrnrs")
    
    CV fold: Fold1
    Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
    
    CV fold: Fold2
    Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
    
    CV fold: Fold3
    Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
    
    CV fold: Fold1
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 6 times in 2 thread(s)...  4.154 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.869 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.617 seconds
    
    CV fold: Fold2
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 6 times in 2 thread(s)...  3.755 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.807 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.525 seconds
    
    CV fold: Fold3
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 6 times in 2 thread(s)...  3.984 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.483 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.622 seconds
    
    CV fold: Fold1
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.497 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
         - Could not obtain meaningful lengthscales.
      2) Running local optimum search...
         - Convergence Not Found. Trying again with tighter parameters...
         - Convergence Not Found. Trying again with tighter parameters...        5.512 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.525 seconds
    
    CV fold: Fold2
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.598 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        11.724 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.374 seconds
    
    CV fold: Fold3
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.572 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
         - Could not obtain meaningful lengthscales.
      2) Running local optimum search...        0.667 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.485 seconds
    
    CV fold: Fold1
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.386 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.41 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.329 seconds
    
    CV fold: Fold2
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.298 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.38 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.355 seconds
    
    CV fold: Fold3
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.515 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
      2) Running local optimum search...        0.46 seconds
      3) Running FUN 2 times in 2 thread(s)...  0.36 seconds
    
    CV fold: Fold1
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.367 seconds
    
    Starting Epoch 1 
      1) Fitting Gaussian Process...
    Saving _problems/test-surv_xgboost_aft-116.R
    
    CV fold: Fold1
    
    Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
    ... reducing initialization grid to 10 rows.
    
    Registering parallel backend using 2 cores.
    
    Running initial scoring function 10 times in 2 thread(s)...  3.067 seconds
        subsample colsample_bytree min_child_weight learning_rate max_depth
            <num>            <num>            <num>         <num>     <num>
     1:       0.6              0.8                5           0.2         1
     2:       1.0              0.8                5           0.1         5
     3:       0.8              0.8                5           0.1         1
     4:       0.6              0.8                5           0.2         5
     5:       1.0              0.8                1           0.1         5
     6:       0.8              0.8                5           0.1         5
     7:       0.6              1.0                1           0.1         5
     8:       0.6              1.0                5           0.2         5
     9:       1.0              1.0                5           0.1         5
    10:       0.6              1.0                1           0.2         1
                                                                errorMessage
                                                                      <char>
     1: FUN returned these elements with length > 1: Score,metric_optim_mean
     2: FUN returned these elements with length > 1: Score,metric_optim_mean
     3: FUN returned these elements with length > 1: Score,metric_optim_mean
     4: FUN returned these elements with length > 1: Score,metric_optim_mean
     5: FUN returned these elements with length > 1: Score,metric_optim_mean
     6: FUN returned these elements with length > 1: Score,metric_optim_mean
     7: FUN returned these elements with length > 1: Score,metric_optim_mean
     8: FUN returned these elements with length > 1: Score,metric_optim_mean
     9: FUN returned these elements with length > 1: Score,metric_optim_mean
    10: FUN returned these elements with length > 1: Score,metric_optim_mean
    Saving _problems/test-surv_xgboost_cox-115.R
    [ FAIL 2 | WARN 0 | SKIP 1 | PASS 11 ]
    
    ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
    • On CRAN (1): 'test-lints.R:10:5'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-surv_xgboost_aft.R:116:5'): test nested cv, bayesian - surv_xgboost_aft ──
    Error in `if (r == 0) stop("Results from FUN have 0 variance, cannot build GP.")`: missing value where TRUE/FALSE needed
    Backtrace:
         ▆
      1. └─surv_xgboost_aft_optimizer$execute() at test-surv_xgboost_aft.R:116:5
      2.   └─mlexperiments:::.run_cv(self = self, private = private)
      3.     └─mlexperiments:::.fold_looper(self, private)
      4.       ├─base::do.call(private$cv_run_model, run_args)
      5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
      6.         ├─base::do.call(.cv_run_nested_model, args)
      7.         └─mlexperiments (local) `<fn>`(...)
      8.           └─hparam_tuner$execute(k = self$k_tuning)
      9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
     10.               └─mlexperiments:::.run_optimizer(...)
     11.                 └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper)
     12.                   ├─base::do.call(...)
     13.                   └─mlexperiments (local) `<fn>`(...)
     14.                     ├─base::do.call(ParBayesianOptimization::bayesOpt, args)
     15.                     └─ParBayesianOptimization (local) `<fn>`(...)
     16.                       └─ParBayesianOptimization::addIterations(...)
     17.                         └─ParBayesianOptimization::updateGP(...)
     18.                           └─ParBayesianOptimization:::zeroOneScale(scoreSummary$Score)
    ── Error ('test-surv_xgboost_cox.R:115:5'): test nested cv, bayesian - surv_xgboost_cox ──
    Error in `(function (FUN, bounds, saveFile = NULL, initGrid, initPoints = 4, iters.n = 3, iters.k = 1, otherHalting = list(timeLimit = Inf, minUtility = 0), acq = "ucb", kappa = 2.576, eps = 0, parallel = FALSE, gsPoints = pmax(100, length(bounds)^3), convThresh = 1e+08, acqThresh = 1, errorHandling = "stop", plotProgress = FALSE, verbose = 1, ...)  {     startT <- Sys.time()     optObj <- list()     class(optObj) <- "bayesOpt"     optObj$FUN <- FUN     optObj$bounds <- bounds     optObj$iters <- 0     optObj$initPars <- list()     optObj$optPars <- list()     optObj$GauProList <- list()     optObj <- changeSaveFile(optObj, saveFile)     checkParameters(bounds, iters.n, iters.k, otherHalting, acq, acqThresh, errorHandling, plotProgress, parallel, verbose)     boundsDT <- boundsToDT(bounds)     otherHalting <- formatOtherHalting(otherHalting)     if (missing(initGrid) + missing(initPoints) != 1)          stop("Please provide 1 of initGrid or initPoints, but not both.")     if (!missing(initGrid)) {         setDT(initGrid)         inBounds <- checkBounds(initGrid, bounds)         inBounds <- as.logical(apply(inBounds, 1, prod))         if (any(!inBounds))              stop("initGrid not within bounds.")         optObj$initPars$initialSample <- "User Provided Grid"         initPoints <- nrow(initGrid)     }     else {         initGrid <- randParams(boundsDT, initPoints)         optObj$initPars$initialSample <- "Latin Hypercube Sampling"     }     optObj$initPars$initGrid <- initGrid     if (nrow(initGrid) <= 2)          stop("Cannot initialize with less than 3 samples.")     optObj$initPars$initPoints <- nrow(initGrid)     if (initPoints <= length(bounds))          stop("initPoints must be greater than the number of FUN inputs.")     sinkFile <- file()     on.exit({         while (sink.number() > 0) sink()         close(sinkFile)     })     `%op%` <- ParMethod(parallel)     if (parallel)          Workers <- getDoParWorkers()     else Workers <- 1     if (verbose > 0)          cat("\nRunning initial scoring function", nrow(initGrid), "times in", Workers, "thread(s)...")     sink(file = sinkFile)     tm <- system.time(scoreSummary <- foreach(iter = 1:nrow(initGrid), .options.multicore = list(preschedule = FALSE), .combine = list, .multicombine = TRUE, .inorder = FALSE, .errorhandling = "pass", .verbose = FALSE) %op% {         Params <- initGrid[get("iter"), ]         Elapsed <- system.time(Result <- tryCatch({             do.call(what = FUN, args = as.list(Params))         }, error = function(e) e))         if (any(class(Result) %in% c("simpleError", "error", "condition")))              return(Result)         if (!inherits(x = Result, what = "list"))              stop("Object returned from FUN was not a list.")         resLengths <- lengths(Result)         if (!any(names(Result) == "Score"))              stop("FUN must return list with element 'Score' at a minimum.")         if (!is.numeric(Result$Score))              stop("Score returned from FUN was not numeric.")         if (any(resLengths != 1)) {             badReturns <- names(Result)[which(resLengths != 1)]             stop("FUN returned these elements with length > 1: ", paste(badReturns, collapse = ","))         }         data.table(Params, Elapsed = Elapsed[[3]], as.data.table(Result))     })[[3]]     while (sink.number() > 0) sink()     if (verbose > 0)          cat(" ", tm, "seconds\n")     se <- which(sapply(scoreSummary, function(cl) any(class(cl) %in% c("simpleError", "error", "condition"))))     if (length(se) > 0) {         print(data.table(initGrid[se, ], errorMessage = sapply(scoreSummary[se], function(x) x$message)))         stop("Errors encountered in initialization are listed above.")     }     else {         scoreSummary <- rbindlist(scoreSummary)     }     scoreSummary[, `:=`(("gpUtility"), rep(as.numeric(NA), nrow(scoreSummary)))]     scoreSummary[, `:=`(("acqOptimum"), rep(FALSE, nrow(scoreSummary)))]     scoreSummary[, `:=`(("Epoch"), rep(0, nrow(scoreSummary)))]     scoreSummary[, `:=`(("Iteration"), 1:nrow(scoreSummary))]     scoreSummary[, `:=`(("inBounds"), rep(TRUE, nrow(scoreSummary)))]     scoreSummary[, `:=`(("errorMessage"), rep(NA, nrow(scoreSummary)))]     extraRet <- setdiff(names(scoreSummary), c("Epoch", "Iteration", boundsDT$N, "inBounds", "Elapsed", "Score", "gpUtility", "acqOptimum"))     setcolorder(scoreSummary, c("Epoch", "Iteration", boundsDT$N, "gpUtility", "acqOptimum", "inBounds", "Elapsed", "Score", extraRet))     if (any(scoreSummary$Elapsed < 1) & acq == "eips") {         cat("\n   FUN elapsed time is too low to be precise. Switching acq to 'ei'.\n")         acq <- "ei"     }     optObj$optPars$acq <- acq     optObj$optPars$kappa <- kappa     optObj$optPars$eps <- eps     optObj$optPars$parallel <- parallel     optObj$optPars$gsPoints <- gsPoints     optObj$optPars$convThresh <- convThresh     optObj$optPars$acqThresh <- acqThresh     optObj$scoreSummary <- scoreSummary     optObj$GauProList$gpUpToDate <- FALSE     optObj$iters <- nrow(scoreSummary)     optObj$stopStatus <- "OK"     optObj$elapsedTime <- as.numeric(difftime(Sys.time(), startT, units = "secs"))     saveSoFar(optObj, 0)     optObj <- addIterations(optObj, otherHalting = otherHalting, iters.n = iters.n, iters.k = iters.k, parallel = parallel, plotProgress = plotProgress, errorHandling = errorHandling, saveFile = saveFile, verbose = verbose, ...)     return(optObj) })(FUN = function (...)  {     kwargs <- list(...)     args <- .method_params_refactor(kwargs, method_helper)     set.seed(self$seed)     res <- do.call(private$fun_bayesian_scoring_function, args)     if (isFALSE(self$metric_optimization_higher_better)) {         res$Score <- as.numeric(I(res$Score * -1L))     }     return(res) }, bounds = list(subsample = c(0.2, 1), colsample_bytree = c(0.2, 1), min_child_weight = c(1L, 10L), learning_rate = c(0.1, 0.2), max_depth = c(1L, 10L)), initGrid = structure(list(subsample = c(0.6, 1, 0.8, 0.6, 1, 0.8, 0.6, 0.6, 1, 0.6), colsample_bytree = c(0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 1, 1, 1), min_child_weight = c(5, 5, 5, 5, 1, 5, 1, 5, 5, 1), learning_rate = c(0.2, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.2, 0.1, 0.2), max_depth = c(1, 5, 1, 5, 5, 5, 5, 5, 5, 1)), out.attrs = list(dim = c(objective = 1L,  eval_metric = 1L, subsample = 3L, colsample_bytree = 3L, min_child_weight = 2L, learning_rate = 2L, max_depth = 2L), dimnames = list(objective = "objective=survival:cox", eval_metric = "eval_metric=cox-nloglik", subsample = c("subsample=0.6", "subsample=0.8", "subsample=1.0"), colsample_bytree = c("colsample_bytree=0.6", "colsample_bytree=0.8", "colsample_bytree=1.0"), min_child_weight = c("min_child_weight=1", "min_child_weight=5"), learning_rate = c("learning_rate=0.1", "learning_rate=0.2"), max_depth = c("max_depth=1",  "max_depth=5"))), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55d8ef75ffc0>), iters.n = 2L, iters.k = 2L, otherHalting = list(timeLimit = Inf, minUtility = 0), acq = "ucb", kappa = 3.5, eps = 0, parallel = TRUE, gsPoints = 125, convThresh = 1e+08, acqThresh = 1, errorHandling = "stop", plotProgress = FALSE, verbose = 1)`: Errors encountered in initialization are listed above.
    Backtrace:
         ▆
      1. └─surv_xgboost_cox_optimizer$execute() at test-surv_xgboost_cox.R:115:5
      2.   └─mlexperiments:::.run_cv(self = self, private = private)
      3.     └─mlexperiments:::.fold_looper(self, private)
      4.       ├─base::do.call(private$cv_run_model, run_args)
      5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
      6.         ├─base::do.call(.cv_run_nested_model, args)
      7.         └─mlexperiments (local) `<fn>`(...)
      8.           └─hparam_tuner$execute(k = self$k_tuning)
      9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
     10.               └─mlexperiments:::.run_optimizer(...)
     11.                 └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper)
     12.                   ├─base::do.call(...)
     13.                   └─mlexperiments (local) `<fn>`(...)
     14.                     ├─base::do.call(ParBayesianOptimization::bayesOpt, args)
     15.                     └─ParBayesianOptimization (local) `<fn>`(...)
    
    [ FAIL 2 | WARN 0 | SKIP 1 | PASS 11 ]
    Error:
    ! Test failures.
    Execution halted

Package: modeltime
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘getting-started-with-modeltime.Rmd’ using rmarkdown
  
  Quitting from getting-started-with-modeltime.Rmd:162-171 [unnamed-chunk-9]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `switch()`:
  ! EXPR must be a length 1 vector
  ---
  Backtrace:
      ▆
   1. ├─... %>% ...
   2. ├─generics::fit(...)
   3. ├─parsnip::fit.model_spec(...)
   4. │ └─parsnip:::form_xy(...)
   5. │   └─parsnip:::xy_xy(...)
   6. │     └─parsnip:::eval_mod(...)
   7. │       └─rlang::eval_tidy(e, env = envir, ...)
   8. └─modeltime::auto_arima_xgboost_fit_impl(...)
   9.   └─modeltime::xgboost_predict(fit_xgboost, newdata = xreg_tbl)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'getting-started-with-modeltime.Rmd' failed with diagnostics:
  EXPR must be a length 1 vector
  --- failed re-building ‘getting-started-with-modeltime.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘getting-started-with-modeltime.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: modeltime
Check: tests
New result: ERROR
    Running ‘testthat.R’ [14s/14s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > 
    > library(xgboost)
    > library(randomForest)
    randomForest 4.7-1.2
    Type rfNews() to see new features/changes/bug fixes.
    > library(thief)
    Loading required package: forecast
    > library(smooth)
    Loading required package: greybox
    Package "greybox", v2.0.6 loaded.
    
    This is package "smooth", v4.3.1
    
    > library(greybox)
    > 
    > library(stats)
    > 
    > library(tidymodels)
    ── Attaching packages ────────────────────────────────────── tidymodels 1.4.1 ──
    ✔ broom        1.0.10     ✔ recipes      1.3.1 
    ✔ dials        1.4.2      ✔ rsample      1.3.1 
    ✔ dplyr        1.1.4      ✔ tailor       0.1.0 
    ✔ ggplot2      4.0.1      ✔ tidyr        1.3.1 
    ✔ infer        1.0.9      ✔ tune         2.0.1 
    ✔ modeldata    1.5.1      ✔ workflows    1.3.0 
    ✔ parsnip      1.4.0      ✔ workflowsets 1.1.1 
    ✔ purrr        1.2.0      ✔ yardstick    1.3.2 
    ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
    ✖ yardstick::accuracy() masks smooth::accuracy(), greybox::accuracy(), forecast::accuracy()
    ✖ dplyr::combine()      masks randomForest::combine()
    ✖ purrr::discard()      masks scales::discard()
    ✖ dplyr::filter()       masks stats::filter()
    ✖ dplyr::lag()          masks stats::lag()
    ✖ ggplot2::margin()     masks randomForest::margin()
    ✖ parsnip::pls()        masks smooth::pls()
    ✖ tidyr::spread()       masks greybox::spread()
    ✖ recipes::step()       masks stats::step()
    > library(parsnip)
    > library(workflows)
    > library(rsample)
    > library(recipes)
    > library(tune)
    > library(dials)
    > library(yardstick)
    > library(slider)
    > 
    > library(timetk)
    > library(modeltime)
    > 
    > test_check("modeltime")
    Saving _problems/test-algo-prophet_boost-120.R
    [ FAIL 1 | WARN 0 | SKIP 80 | PASS 0 ]
    
    ══ Skipped tests (80) ══════════════════════════════════════════════════════════
    • On CRAN (80): 'test-algo-adam_reg-Adam.R:12:5',
      'test-algo-adam_reg-Adam.R:111:5', 'test-algo-adam_reg-auto_adam.R:13:5',
      'test-algo-adam_reg-auto_adam.R:63:5',
      'test-algo-adam_reg-auto_adam.R:120:5', 'test-algo-arima_boost-Arima.R:57:5',
      'test-algo-arima_boost-Arima.R:114:5', 'test-algo-arima_boost-Arima.R:190:5',
      'test-algo-arima_boost-auto_arima.R:41:5',
      'test-algo-arima_boost-auto_arima.R:98:5',
      'test-algo-arima_boost-auto_arima.R:172:5',
      'test-algo-arima_reg-Arima.R:35:5', 'test-algo-arima_reg-Arima.R:89:5',
      'test-algo-arima_reg-Arima.R:145:5', 'test-algo-arima_reg-auto_arima.R:20:5',
      'test-algo-arima_reg-auto_arima.R:76:5',
      'test-algo-arima_reg-auto_arima.R:137:5',
      'test-algo-exp_smoothing-ets.R:25:5', 'test-algo-exp_smoothing-ets.R:82:5',
      'test-algo-exp_smoothing-ets.R:159:5', 'test-algo-exp_smoothing-ets.R:234:5',
      'test-algo-exp_smoothing-ets.R:312:5', 'test-algo-exp_smoothing-ets.R:373:5',
      'test-algo-nnetar_reg.R:21:5', 'test-algo-nnetar_reg.R:146:5',
      'test-algo-prophet_boost.R:44:5', 'test-algo-prophet_boost.R:206:5',
      'test-algo-prophet_boost.R:313:5', 'test-algo-prophet_reg.R:35:5',
      'test-algo-prophet_reg.R:105:5', 'test-algo-prophet_reg.R:178:5',
      'test-algo-prophet_reg.R:266:5', 'test-algo-seasonal_decomp_arima.R:8:5',
      'test-algo-seasonal_decomp_ets.R:10:5',
      'test-algo-seasonal_reg_tbats.R:20:5', 'test-algo-seasonal_reg_tbats.R:35:5',
      'test-algo-seasonal_reg_tbats.R:93:5', 'test-algo-temporal_hierarchy.R:8:5',
      'test-algo-window_reg.R:24:5', 'test-algo-window_reg.R:69:5',
      'test-algo-window_reg.R:100:5', 'test-algo-window_reg.R:153:5',
      'test-algo-window_reg.R:206:5', 'test-algo-window_reg.R:241:5',
      'test-algo-window_reg.R:293:5', 'test-algo-window_reg.R:363:5',
      'test-algo-window_reg.R:402:5', 'test-conf_by_id.R:6:5',
      'test-default_accuracy_metric_sets.R:9:5',
      'test-default_accuracy_metric_sets.R:29:5',
      'test-developer-tools-constructor.R:10:5',
      'test-developer-tools-xregs.R:26:5', 'test-developer-tools-xregs.R:47:5',
      'test-extended_accuracy_metric_set.R:7:5',
      'test-extended_accuracy_metric_set.R:28:5', 'test-fit_workflowsets.R:13:5',
      'test-helpers-combine-modeltime-tables.R:8:5',
      'test-helpers-pull_parsnip_preprocessor.R:14:5',
      'test-helpers-pull_parsnip_preprocessor.R:33:5',
      'test-helpers-pull_parsnip_preprocessor.R:51:5',
      'test-helpers-pull_parsnip_preprocessor.R:69:5',
      'test-helpers-update-modeltime-tables.R:9:5',
      'test-modeltime_residuals.R:6:5',
      'test-modeltime_table-forecast-accuracy-refitting.R:20:5',
      'test-modeltime_table-forecast-accuracy-refitting.R:84:5',
      'test-modeltime_table-forecast-accuracy-refitting.R:149:5',
      'test-modeltime_table-no-calib-refit.R:16:5', 'test-nested-modeltime.R:10:5',
      'test-panel-data.R:8:5', 'test-recursive-chunk-uneven.R:9:5',
      'test-recursive-chunk-uneven.R:331:5', 'test-recursive-chunk.R:9:5',
      'test-recursive.R:9:3', 'test-refit-parallel.R:5:5',
      'test-results-accuracy-tables.R:15:5',
      'test-results-accuracy-tables.R:149:5', 'test-results-forecast-plots.R:14:5',
      'test-results-forecast-plots.R:87:5', 'test-results-residuals-tests.R:9:5',
      'test-tune_workflows.R:8:5'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-algo-prophet_boost.R:118:5'): prophet_boost: prophet, XREGS ────
    Error in `switch(object$params$objective, `reg:linear` = , `reg:logistic` = , `binary:logistic` = res, `binary:logitraw` = stats::binomial()$linkinv(res), `multi:softprob` = matrix(res, ncol = object$params$num_class, byrow = TRUE), res)`: EXPR must be a length 1 vector
    Backtrace:
        ▆
     1. ├─model_spec %>% ... at test-algo-prophet_boost.R:118:5
     2. ├─generics::fit(...)
     3. ├─parsnip::fit.model_spec(...)
     4. │ └─parsnip:::form_xy(...)
     5. │   └─parsnip:::xy_xy(...)
     6. │     └─parsnip:::eval_mod(...)
     7. │       └─rlang::eval_tidy(e, env = envir, ...)
     8. └─modeltime::prophet_xgboost_fit_impl(...)
     9.   └─modeltime::xgboost_predict(fit_xgboost, newdata = xreg_tbl)
    
    [ FAIL 1 | WARN 0 | SKIP 80 | PASS 0 ]
    Error:
    ! Test failures.
    Execution halted

Package: modeltime.ensemble
Check: tests
New result: ERROR
    Running ‘testthat.R’ [36s/35s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > 
    > # Machine Learning
    > library(tidymodels)
    ── Attaching packages ────────────────────────────────────── tidymodels 1.4.1 ──
    ✔ broom        1.0.10     ✔ recipes      1.3.1 
    ✔ dials        1.4.2      ✔ rsample      1.3.1 
    ✔ dplyr        1.1.4      ✔ tailor       0.1.0 
    ✔ ggplot2      4.0.1      ✔ tidyr        1.3.1 
    ✔ infer        1.0.9      ✔ tune         2.0.1 
    ✔ modeldata    1.5.1      ✔ workflows    1.3.0 
    ✔ parsnip      1.4.0      ✔ workflowsets 1.1.1 
    ✔ purrr        1.2.0      ✔ yardstick    1.3.2 
    ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
    ✖ purrr::discard() masks scales::discard()
    ✖ dplyr::filter()  masks stats::filter()
    ✖ dplyr::lag()     masks stats::lag()
    ✖ recipes::step()  masks stats::step()
    > library(modeltime)
    > library(modeltime.ensemble)
    Loading required package: modeltime.resample
    > library(modeltime.resample)
    > 
    > # Model dependencies
    > library(xgboost)
    > library(glmnet)
    Loading required package: Matrix
    
    Attaching package: 'Matrix'
    
    The following objects are masked from 'package:tidyr':
    
        expand, pack, unpack
    
    Loaded glmnet 4.1-10
    > 
    > # Core Packages
    > library(timetk)
    > library(lubridate)
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
        date, intersect, setdiff, union
    
    > 
    > test_check("modeltime.ensemble")
    ── Modeltime Ensemble ───────────────────────────────────────────
    Ensemble of 3 Models (WEIGHTED) 
    
    # Modeltime Table
    # A tibble: 3 × 4
      .model_id .model     .model_desc             .loadings
          <int> <list>     <chr>                       <dbl>
    1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]     0.5  
    2         2 <workflow> PROPHET                     0.333
    3         3 <workflow> GLMNET                      0.167
    
    
    
    
    
    
    
    
    Saving _problems/test-panel-data-25.R
    [ FAIL 1 | WARN 0 | SKIP 4 | PASS 61 ]
    
    ══ Skipped tests (4) ═══════════════════════════════════════════════════════════
    • On CRAN (4): 'test-conf_by_id.R:6:5', 'test-ensemble_average.R:57:5',
      'test-ensemble_model_spec.R:55:5', 'test-nested-ensembles.R:189:5'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-panel-data.R:16:1'): (code run outside of `test_that()`) ───────
    Error in `switch(object$params$objective, `reg:linear` = , `reg:logistic` = , `binary:logistic` = res, `binary:logitraw` = stats::binomial()$linkinv(res), `multi:softprob` = matrix(res, ncol = object$params$num_class, byrow = TRUE), res)`: EXPR must be a length 1 vector
    Backtrace:
         ▆
      1. ├─... %>% fit(data_set) at test-panel-data.R:16:1
      2. ├─generics::fit(., data_set)
      3. ├─workflows:::fit.workflow(., data_set)
      4. │ └─workflows::.fit_model(workflow, control)
      5. │   ├─generics::fit(action_model, workflow = workflow, control = control)
      6. │   └─workflows:::fit.action_model(...)
      7. │     └─workflows:::fit_from_xy(spec, mold, case_weights, control_parsnip)
      8. │       ├─generics::fit_xy(...)
      9. │       └─parsnip::fit_xy.model_spec(...)
     10. │         └─parsnip:::xy_xy(...)
     11. │           └─parsnip:::eval_mod(...)
     12. │             └─rlang::eval_tidy(e, env = envir, ...)
     13. └─modeltime::prophet_xgboost_fit_impl(...)
     14.   └─modeltime::xgboost_predict(fit_xgboost, newdata = xreg_tbl)
    
    [ FAIL 1 | WARN 0 | SKIP 4 | PASS 61 ]
    Error:
    ! Test failures.
    Execution halted

Package: nlpred
Check: examples
New result: ERROR
  Running examples in ‘nlpred-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: xgboost_wrapper
  > ### Title: Wrapper for fitting eXtreme gradient boosting via 'xgboost'
  > ### Aliases: xgboost_wrapper
  > 
  > ### ** Examples
  > 
  > # simulate data
  > # make list of training data
  > train_X <- data.frame(x1 = runif(50))
  > train_Y <- rbinom(50, 1, plogis(train_X$x1))
  > train <- list(Y = train_Y, X = train_X)
  > # make list of test data
  > test_X <- data.frame(x1 = runif(50))
  > test_Y <- rbinom(50, 1, plogis(train_X$x1))
  > test <- list(Y = test_Y, X = test_X)
  > # fit xgboost
  > xgb_wrap <- xgboost_wrapper(train = train, test = test)
  Warning in throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
    Parameter(s) have been removed from this function: params, save_period. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Error in xgboost::xgboost(data = xgmat, objective = "binary:logistic",  : 
    argument "y" is missing, with no default
  Calls: xgboost_wrapper ... <Anonymous> -> process.y.margin.and.objective -> NROW
  Execution halted

Package: nlpred
Check: tests
New result: ERROR
    Running ‘testthat.R’ [9s/9s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(nlpred)
    Loading required package: data.table
    > 
    > test_check("nlpred")
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
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    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 |
    Multistart 1 of 1 /
    Multistart 1 of 1 |
    Multistart 1 of 1 |
                       
    Saving _problems/testWrappers-13.R
    [ FAIL 1 | WARN 4 | SKIP 0 | PASS 40 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('testWrappers.R:12:3'): wrappers work ────────────────────────────────
    <getvarError/missingArgError/error/condition>
    Error in `xgboost::xgboost(data = xgmat, objective = "binary:logistic", nrounds = ntrees, max_depth = max_depth, min_child_weight = minobspernode, eta = shrinkage, verbose = verbose, nthread = nthread, params = params, save_period = save_period)`: argument "y" is missing, with no default
    Backtrace:
        ▆
     1. └─nlpred (local) check_wrapper(paste0(wrap, "_wrapper"), test = test, train = train) at testWrappers.R:31:17
     2.   ├─base::do.call(wrapper, args = list(train = train, test = test)) at testWrappers.R:12:17
     3.   └─nlpred::xgboost_wrapper(train = `<list>`, test = `<list>`)
     4.     └─xgboost::xgboost(...)
     5.       └─xgboost:::process.y.margin.and.objective(...)
     6.         └─base::NROW(y)
    
    [ FAIL 1 | WARN 4 | SKIP 0 | PASS 40 ]
    Error:
    ! Test failures.
    Execution halted

Package: ParBayesianOptimization
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘functionMaximization.Rmd’ using rmarkdown
    0.462 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.123 seconds
    3) Running FUN 1 times in 1 thread(s)...  0.145 seconds
  
  Starting Epoch 2 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.221 seconds
    3) Running FUN 1 times in 1 thread(s)...  0.147 seconds
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘functionMaximization.Rmd’
  
  --- re-building ‘multiPointSampling.Rmd’ using rmarkdown
  --- finished re-building ‘multiPointSampling.Rmd’
  
  --- re-building ‘tuningHyperparameters.Rmd’ using rmarkdown
    1.308 seconds
     max_depth min_child_weight subsample                                         errorMessage
         <num>            <num>     <num>                                               <char>
  1:         9         5.863591 0.2585819 FUN returned these elements with length > 1: nrounds
  2:         4        10.154185 0.5230172 FUN returned these elements with length > 1: nrounds
  3:         6        24.487949 0.8622225 FUN returned these elements with length > 1: nrounds
  4:         2        17.988070 0.6821260 FUN returned these elements with length > 1: nrounds
  
  Quitting from tuningHyperparameters.Rmd:107-116 [unnamed-chunk-4]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `bayesOpt()`:
  ! Errors encountered in initialization are listed above.
  ---
  Backtrace:
      ▆
   1. └─ParBayesianOptimization::bayesOpt(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'tuningHyperparameters.Rmd' failed with diagnostics:
  Errors encountered in initialization are listed above.
  --- failed re-building ‘tuningHyperparameters.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘tuningHyperparameters.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: pdp
Check: tests
New result: ERROR
    Running ‘tinytest.R’ [81s/81s]
  Running the tests in ‘tests/tinytest.R’ failed.
  Complete output:
    > # Run tests in local environment
    > if (requireNamespace("tinytest", quietly = TRUE)) {
    +   home <- length(unclass(packageVersion("pdp"))[[1]]) == 4
    +   tinytest::test_package("pdp", at_home = home)
    + }
    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    0 tests    
    test_cats_argument.R..........    4 tests OK randomForest 4.7-1.2
    Type rfNews() to see new features/changes/bug fixes.
    
    test_cats_argument.R..........    5 tests OK 0.3s
    
    test_exemplar.R...............    0 tests    
    test_exemplar.R...............    0 tests    
    test_exemplar.R...............    0 tests    
    test_exemplar.R...............    1 tests OK 6ms
    
    test_get_training_data.R......    0 tests    
    Attaching package: 'ggplot2'
    
    The following object is masked from 'package:randomForest':
    
        margin
    
    
    test_get_training_data.R......    2 tests OK 
    test_get_training_data.R......    5 tests OK 
    test_get_training_data.R......    8 tests OK 
    Attaching package: 'ranger'
    
    The following object is masked from 'package:randomForest':
    
        importance
    
    
    test_get_training_data.R......   10 tests OK 3.8s
    
    test_pkg_C50.R................    0 tests    
    test_pkg_C50.R................    4 tests OK 86ms
    
    test_pkg_MASS.R...............    0 tests    
    test_pkg_MASS.R...............    0 tests    
    test_pkg_MASS.R...............    8 tests OK 2.0s
    
    test_pkg_e1071.R..............    0 tests    
    test_pkg_e1071.R..............    0 tests    
    test_pkg_e1071.R..............    0 tests    
    test_pkg_e1071.R..............    0 tests    
    Attaching package: 'e1071'
    
    The following object is masked from 'package:ggplot2':
    
        element
    
    
    test_pkg_e1071.R..............    8 tests OK 1.2s
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
        as.Date, as.Date.numeric
    
    
    test_pkg_party.R..............   12 tests OK 1.2s
    
    test_pkg_ranger.R.............    0 tests    
    test_pkg_ranger.R.............    0 tests    
    test_pkg_ranger.R.............    0 tests    3ms
    
    test_pkg_stats.R..............   10 tests OK 1.4s
    
    test_pkg_xgboost.R............    0 tests    
    test_pkg_xgboost.R............    0 tests    
    test_pkg_xgboost.R............    0 tests    
    test_pkg_xgboost.R............    0 tests    
    test_pkg_xgboost.R............    0 tests    
    test_pkg_xgboost.R............    0 tests    Error in if (object$params$objective %in% c("reg:gamma", "reg:linear",  : 
      argument is of length zero
    Calls: <Anonymous> ... partial.default -> get_task -> get_task.xgb.Booster
    In addition: Warning messages:
    1: In partial.default(fit4, pred.var = "x.4", prob = TRUE, ice = TRUE,  :
      Centering may result in probabilities outside of [0, 1].
    2: glm.fit: algorithm did not converge 
    3: glm.fit: fitted probabilities numerically 0 or 1 occurred 
    4: In partial.default(fit2_glm, pred.var = "x.3", prob = TRUE, ice = TRUE,  :
      Centering may result in probabilities outside of [0, 1].
    5: In throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
      Parameter(s) have been removed from this function: params, save_period. This warning will become an error in a future version.
    6: In throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
      Passed unrecognized parameters: verbose. This warning will become an error in a future version.
    7: In throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
      Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
    8: In throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
      Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
    Execution halted

Package: personalized
Check: whether package can be installed
New result: ERROR
  Installation failed.

Package: pmml
Check: tests
New result: ERROR
    Running ‘testthat.R’ [42s/42s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(pmml, quietly = T)
    > 
    > test_check("pmml")
    Saving _problems/test_pmml.xgb.Booster-59.R
    Saving _problems/test_pmml.xgb.Booster-74.R
    Saving _problems/test_pmml.xgb.Booster-102.R
    [ FAIL 6 | WARN 26 | SKIP 51 | PASS 356 ]
    
    ══ Skipped tests (51) ══════════════════════════════════════════════════════════
    • On CRAN (48): 'test_pmml.iForest.R:6:3',
      'test_pmml_integration_ARIMA.R:109:3', 'test_pmml_integration_ARIMA.R:184:3',
      'test_pmml_integration_ARIMA.R:269:3',
      'test_pmml_integration_e1071_svm.R:27:3',
      'test_pmml_integration_e1071_svm.R:275:3', 'test_pmml_integration_lm.R:13:3',
      'test_pmml_integration_lm.R:123:3', 'test_pmml_integration_lm.R:175:3',
      'test_pmml_integration_other.R:120:3', 'test_pmml_integration_other.R:167:3',
      'test_pmml_integration_other.R:265:3', 'test_pmml_integration_other.R:439:3',
      'test_pmml_integration_other.R:607:3', 'test_pmml_integration_other.R:692:3',
      'test_pmml_integration_other.R:849:3',
      'test_pmml_integration_other.R:1062:3',
      'test_pmml_integration_other.R:1322:3',
      'test_pmml_integration_other.R:1442:3',
      'test_pmml_integration_other.R:1547:3',
      'test_pmml_integration_other.R:1633:3',
      'test_pmml_integration_other.R:1822:3',
      'test_pmml_integration_transformations.R:19:3',
      'test_pmml_integration_transformations.R:319:3',
      'test_pmml_integration_transformations.R:354:3',
      'test_pmml_integration_transformations.R:377:3',
      'test_pmml_integration_transformations.R:407:3',
      'test_pmml_integration_transformations.R:469:3',
      'test_pmml_integration_xgboost.R:21:3', 'test_schema_validation.R:135:3',
      'test_schema_validation.R:183:3', 'test_schema_validation.R:204:3',
      'test_schema_validation.R:248:3', 'test_schema_validation.R:343:3',
      'test_schema_validation.R:426:3', 'test_schema_validation.R:458:3',
      'test_schema_validation.R:500:3', 'test_schema_validation.R:603:3',
      'test_schema_validation.R:795:3', 'test_schema_validation.R:933:3',
      'test_schema_validation.R:1008:3', 'test_schema_validation.R:1045:3',
      'test_schema_validation.R:1077:3', 'test_schema_validation.R:1146:3',
      'test_schema_validation.R:1193:3', 'test_schema_validation.R:1429:3',
      'test_schema_validation.R:1510:3', 'test_schema_validation.R:1540:3'
    • skip (2): 'test_pmml_integration_lm.R:147:3',
      'test_pmml_integration_transformations.R:439:3'
    • skip until export issue is resolved (1): 'test_pmml.nnet.R:66:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test_pmml.miningschema.R:29:3'): invalidValueTreatment attribute is exported correctly for xgboost models ──
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. ├─utils::capture.output(...) at test_pmml.miningschema.R:29:3
     2. │ └─base::withVisible(...elt(i))
     3. └─xgboost::xgboost(...)
     4.   └─xgboost:::process.y.margin.and.objective(...)
    ── Error ('test_pmml.miningschema.R:286:3'): error is thrown if invalidValueTreatment argument is incorrect ──
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. ├─utils::capture.output(...) at test_pmml.miningschema.R:286:3
     2. │ └─base::withVisible(...elt(i))
     3. └─xgboost::xgboost(...)
     4.   └─xgboost:::process.y.margin.and.objective(...)
    ── Error ('test_pmml.xgb.Booster.R:18:3'): discrete variables are one-hot-encoded ──
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. ├─utils::capture.output(...) at test_pmml.xgb.Booster.R:18:3
     2. │ └─base::withVisible(...elt(i))
     3. └─xgboost::xgboost(...)
     4.   └─xgboost:::process.y.margin.and.objective(...)
    ── Failure ('test_pmml.xgb.Booster.R:52:3'): error is thrown when objective = reg:linear ──
    `pmml(...)` threw an error with unexpected message.
    Expected match: "Only the following objectives are supported: multi:softprob, multi:softmax, binary:logistic."
    Actual message: "argument is of length zero"
    Backtrace:
        ▆
     1. ├─testthat::expect_error(...) at test_pmml.xgb.Booster.R:52:3
     2. │ └─testthat:::quasi_capture(...)
     3. │   ├─testthat (local) .capture(...)
     4. │   │ └─base::withCallingHandlers(...)
     5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     6. ├─pmml::pmml(...)
     7. └─pmml::pmml.xgb.Booster(...)
    ── Error ('test_pmml.xgb.Booster.R:70:3'): error is thrown when objective = reg:logistic ──
    Error in `prescreen.objective(objective)`: Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
    Backtrace:
        ▆
     1. └─xgboost::xgboost(...) at test_pmml.xgb.Booster.R:70:3
     2.   └─xgboost:::prescreen.objective(objective)
    ── Error ('test_pmml.xgb.Booster.R:97:3'): error is thrown when objective = binary:logitraw ──
    Error in `prescreen.objective(objective)`: Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
    Backtrace:
        ▆
     1. └─xgboost::xgboost(...) at test_pmml.xgb.Booster.R:97:3
     2.   └─xgboost:::prescreen.objective(objective)
    
    [ FAIL 6 | WARN 26 | SKIP 51 | PASS 356 ]
    Error:
    ! Test failures.
    Execution halted

Package: polle [Old version: 1.6.0, New version: 1.6.1]
Check: tests
New result: ERROR
    Running ‘test-all.R’ [158s/154s]
  Running the tests in ‘tests/test-all.R’ failed.
  Complete output:
    > suppressPackageStartupMessages(library("testthat"))
    > options(Ncpus = 2)
    > data.table::setDTthreads(2)
    > test_check("polle")
    Loading required package: polle
    Loading required package: SuperLearner
    Loading required package: nnls
    Loading required package: gam
    Loading required package: splines
    Loading required package: foreach
    Loaded gam 1.22-6
    
    Super Learner
    Version: 2.0-29
    Package created on 2024-02-06
    
    Saving _problems/test-g_xgboost-10.R
    Saving _problems/test-g_xgboost-42.R
    Saving _problems/test-g_xgboost-71.R
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = objective, nrounds = ntrees,  : 
      argument "y" is missing, with no default
    Saving _problems/test-q_xgboost-32.R
    [ FAIL 4 | WARN 202 | SKIP 0 | PASS 935 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-g_xgboost.R:7:3'): g_xgboost gives the same result as plain xgboost ──
    <getvarError/missingArgError/error/condition>
    Error in `xgboost::xgboost(data = xgboost_data, max_depth = 2, eta = 1, nrounds = 2, objective = "binary:logistic", verbose = FALSE)`: argument "y" is missing, with no default
    Backtrace:
        ▆
     1. └─xgboost::xgboost(...) at test-g_xgboost.R:7:3
     2.   └─xgboost:::process.y.margin.and.objective(...)
     3.     └─base::NROW(y)
    ── Error ('test-g_xgboost.R:41:3'): g_xgboost gives the same result as SL.xgboost ──
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
         ▆
      1. └─polle::fit_g_functions(...) at test-g_xgboost.R:41:3
      2.   └─polle:::fit_g_function(history, g_models)
      3.     └─polle (local) g_model(A = A, H = H, action_set = stage_action_set)
      4.       └─model$estimate(data)
      5.         └─private$fitfun(data, ...)
      6.           ├─base::structure(do.call(private$init.estimate, args), design = summary(xx))
      7.           ├─base::do.call(private$init.estimate, args)
      8.           └─polle (local) `<fn>`(x = `<dbl>`, y = `<dbl>`)
      9.             └─xgboost::xgboost(...)
     10.               └─xgboost:::process.y.margin.and.objective(...)
    ── Error ('test-g_xgboost.R:70:3'): g_xgboost tunes parameters ─────────────────
    Error in `terms.formula(formula)`: '.' in formula and no 'data' argument
    Backtrace:
        ▆
     1. └─polle::fit_g_functions(...) at test-g_xgboost.R:70:3
     2.   └─polle:::fit_g_function(history, g_models)
     3.     └─polle (local) g_model(A = A, H = H, action_set = stage_action_set)
     4.       ├─stats::delete.response(terms(formula))
     5.       ├─stats::terms(formula)
     6.       └─stats::terms.formula(formula)
    ── Error ('test-q_xgboost.R:28:3'): q_xgboost gives the same result as plain xgboost ──
    <getvarError/missingArgError/error/condition>
    Error in `xgboost::xgboost(data = xgboost_data, max_depth = 2, eta = 1, nrounds = 2, verbose = FALSE)`: argument "y" is missing, with no default
    Backtrace:
        ▆
     1. └─xgboost::xgboost(...) at test-q_xgboost.R:28:3
     2.   └─xgboost:::process.y.margin.and.objective(...)
     3.     └─base::NROW(y)
    
    [ FAIL 4 | WARN 202 | SKIP 0 | PASS 935 ]
    Error:
    ! Test failures.
    Execution halted

Package: PoweREST
Check: examples
New result: ERROR
  Running examples in ‘PoweREST-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: fit_XGBoost
  > ### Title: Fit with XGBoost
  > ### Aliases: fit_XGBoost
  > 
  > ### ** Examples
  > 
  > data(power_example)
  > # Fit the local power surface of avg_log2FC_abs between 1 and 2
  > avg_log2FC_abs_1_2<-dplyr::filter(power_example,avg_log2FC_abs>1 & avg_log2FC_abs<2)
  > # Fit the model
  > bst<-fit_XGBoost(power_example$power,avg_log2FC=power_example$avg_log2FC_abs,
  + avg_PCT=power_example$mean_pct,replicates=power_example$sample_size)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: max.depth, verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Error in xgboost::xgboost(data = dtrain, max.depth = max.depth, eta = eta,  : 
    argument "y" is missing, with no default
  Calls: fit_XGBoost ... <Anonymous> -> process.y.margin.and.objective -> NROW
  Execution halted

Package: predictoR
Check: examples
New result: ERROR
  Running examples in ‘predictoR-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: e_xgb_importance
  > ### Title: Var importance XGBoosting
  > ### Aliases: e_xgb_importance
  > 
  > ### ** Examples
  > 
  > model <- traineR::train.xgboost(Species ~ ., data = iris, nrounds = 20)
  Warning in check.deprecation(deprecated_train_params, match.call(), ...) :
    Passed invalid function arguments: eval_metric. These should be passed as a list to argument 'params'. Conversion from argument to 'params' entry will be done automatically, but this behavior will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'watchlist' has been renamed to 'evals'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'feval' has been renamed to 'custom_metric'. This warning will become an error in a future version.
  [1]	train-mlogloss:0.736115 
  [2]	train-mlogloss:0.524235 
  [3]	train-mlogloss:0.387996 
  [4]	train-mlogloss:0.294146 
  [5]	train-mlogloss:0.226824 
  [6]	train-mlogloss:0.177835 
  [7]	train-mlogloss:0.141766 
  [8]	train-mlogloss:0.115002 
  [9]	train-mlogloss:0.094791 
  [10]	train-mlogloss:0.078860 
  [11]	train-mlogloss:0.066746 
  [12]	train-mlogloss:0.057845 
  [13]	train-mlogloss:0.050360 
  [14]	train-mlogloss:0.044290 
  [15]	train-mlogloss:0.039567 
  [16]	train-mlogloss:0.035267 
  [17]	train-mlogloss:0.032581 
  [18]	train-mlogloss:0.030403 
  [19]	train-mlogloss:0.028410 
  [20]	train-mlogloss:0.026969 
  Error in model$prmdt <- `*vtmp*` : 
    ALTLIST classes must provide a Set_elt method [class: XGBAltrepPointerClass, pkg: xgboost]
  Calls: <Anonymous> -> create.model
  Execution halted

Package: radiant.model
Check: examples
New result: ERROR
  Running examples in ‘radiant.model-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: gbt
  > ### Title: Gradient Boosted Trees using XGBoost
  > ### Aliases: gbt
  > 
  > ### ** Examples
  > 
  > ## Not run: 
  > ##D gbt(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
  > ##D gbt(titanic, "survived", c("pclass", "sex")) %>% str()
  > ## End(Not run)
  > gbt(
  +   titanic, "survived", c("pclass", "sex"), lev = "Yes",
  +   early_stopping_rounds = 0, nthread = 1
  + ) %>% summary()
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Error in process.y.margin.and.objective(y, base_margin, objective, params) : 
    Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
  Calls: %>% ... do.call -> <Anonymous> -> process.y.margin.and.objective
  Execution halted

Package: RIIM
Check: examples
New result: ERROR
  Running examples in ‘RIIM-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: IPPW
  > ### Title: Randomization-based inference using inverse post-matching
  > ###   probability weighting (IPPW)
  > ### Aliases: IPPW
  > 
  > ### ** Examples
  > 
  > library(MASS)
  > library(xgboost)
  > library(optmatch)
  > 
  > # Generate data
  > set.seed(1)
  > d = 3
  > n = 30
  > sigma = diag(d)
  > 
  > # Generate X
  > X_d = mvtnorm::rmvnorm(n, mean = rep(0,d), sigma = sigma)
  > 
  > # Generate Z
  > C = -2.5 
  > fx = 0.1*(X_d[,1])^3 + 0.3*(X_d[,2]) + 0.2*log((X_d[,3])^2) + 
  +      abs(X_d[,1]*X_d[,2]) + rnorm(n,0,1) + C
  > p = exp(fx)/(1+exp(fx)) # the probability of receiving the treatment
  > Z = rep(0,length(p))
  > for(i in seq_along(p)){
  +   Z[i] = rbinom(1,1,p[i])
  + }
  > 
  > # Generate Y 
  > Y_0 = 0.2*(X_d[,1])^3 + 0.2*abs(X_d[,2]) + 0.5*abs(X_d[,3]) + rnorm(n,0,1)
  > Y_1 = Y_0 + 1 + 0.3*X_d[,1] + 0.2*X_d[,3]^3
  > Y = (1-Z)*Y_0 + Z*Y_1
  > 
  > # The output
  > est = IPPW(Y,Z,X_d,min.controls = 0.01,max.controls = 100,caliper=FALSE,  
  + calipersd = 0.2,dim=FALSE,gamma=0.1,alpha=0.05)$estimate
  Warning in optmatch::fullmatch(distmat, min.controls, max.controls) :
    Without 'data' argument the order of the match is not guaranteed
      to be the same as your original data.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Error in process.y.margin.and.objective(y, base_margin, objective, params) : 
    Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
  Calls: IPPW -> <Anonymous> -> process.y.margin.and.objective
  Execution halted

Package: SHAPBoost
Check: examples
New result: ERROR
  Running examples in ‘SHAPBoost-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: SHAPBoostEstimator-class
  > ### Title: SHAPBoostEstimator Class
  > ### Aliases: SHAPBoostEstimator-class SHAPBoostEstimator
  > 
  > ### ** Examples
  > 
  > if (requireNamespace("flare", quietly = TRUE)) {
  +   data("eyedata", package = "flare")
  +   shapboost <- SHAPBoostRegressor$new(
  +     max_number_of_features = 1,
  +     evaluator = "lr",
  +     metric = "mae",
  +     siso_ranking_size = 10,
  +     verbose = 0
  +   )
  +   X <- as.data.frame(x)
  +   y <- as.data.frame(y)
  +   subset <- shapboost$fit(X, y)
  + }
  
  Iteration:1
  
  Selected variables:
  
  
  Error in .internal.setinfo.xgb.DMatrix(object, name, info) : 
    [00:31:42] xgboost_R.cc:167: Array or matrix has unsupported type.
  Stack trace:
    [bt] (0) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x798b1) [0x7f7a978798b1]
    [bt] (1) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(+0x6f006) [0x7f7a9786f006]
    [bt] (2) /home/hornik/tmp/CRAN_recheck/Library/xgboost/libs/xgboost.so(XGDMatrixSetInfo_R+0x59) [0x7f7a9786f7d9]
    [bt] (3) /home/hornik/tmp/R/lib/libR.so(+0x103f6e) [0x7f7ab0103f6e]
    [bt] (4) /home/hornik/tmp/R/lib/libR.so(+0x1
  Calls: <Anonymous> ... setinfo.xgb.DMatrix -> .internal.setinfo.xgb.DMatrix
  Execution halted

Package: SHAPforxgboost
Check: examples
New result: ERROR
  Running examples in ‘SHAPforxgboost-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: shap.plot.summary
  > ### Title: SHAP summary plot core function using the long format SHAP
  > ###   values
  > ### Aliases: shap.plot.summary
  > 
  > ### ** Examples
  > 
  > data("iris")
  > X1 = as.matrix(iris[,-5])
  > mod1 = xgboost::xgboost(
  +   data = X1, label = iris$Species, gamma = 0, eta = 1,
  +   lambda = 0, nrounds = 1, verbose = FALSE, nthread = 1)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'gamma' has been renamed to 'min_split_loss'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'lambda' has been renamed to 'reg_lambda'. This warning will become an error in a future version.
  > 
  > # shap.values(model, X_dataset) returns the SHAP
  > # data matrix and ranked features by mean|SHAP|
  > shap_values <- shap.values(xgb_model = mod1, X_train = X1)
  Warning in `[.data.table`(shap_contrib, , `:=`(BIAS, NULL)) :
    Tried to assign NULL to column 'BIAS', but this column does not exist to remove
  > shap_values$mean_shap_score
      setosa versicolor  virginica 
   0.3360065  0.3326459  0.3313476 
  > shap_values_iris <- shap_values$shap_score
  > 
  > # shap.prep() returns the long-format SHAP data from either model or
  > shap_long_iris <- shap.prep(xgb_model = mod1, X_train = X1)
  Warning in `[.data.table`(shap_contrib, , `:=`(BIAS, NULL)) :
    Tried to assign NULL to column 'BIAS', but this column does not exist to remove
  Error in `[.data.table`(setDT(shap$shap_score), , names(shap$mean_shap_score)[1:top_n],  : 
    column not found: [NA]
  Calls: shap.prep ... [ -> [.data.table -> stopf -> raise_condition -> signal
  Execution halted

Package: SHAPforxgboost
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘basic_workflow.Rmd’ using rmarkdown
  
  Quitting from basic_workflow.Rmd:62-87 [unnamed-chunk-3]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `[.data.table`:
  ! column not found: [(Intercept)]
  ---
  Backtrace:
      ▆
   1. └─SHAPforxgboost::shap.prep(fit, X_train = X)
   2.   ├─...[]
   3.   └─data.table:::`[.data.table`(...)
   4.     └─data.table:::stopf(...)
   5.       └─data.table:::raise_condition(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'basic_workflow.Rmd' failed with diagnostics:
  column not found: [(Intercept)]
  --- failed re-building ‘basic_workflow.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘basic_workflow.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: SuperLearner
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘Guide-to-SuperLearner.Rmd’ using rmarkdown
  Boston                  package:MASS                   R Documentation
  
  _H_o_u_s_i_n_g _V_a_l_u_e_s _i_n _S_u_b_u_r_b_s _o_f _B_o_s_t_o_n
  
  _D_e_s_c_r_i_p_t_i_o_n:
  
       The 'Boston' data frame has 506 rows and 14 columns.
  
  _U_s_a_g_e:
  
       Boston
       
  _F_o_r_m_a_t:
  
       This data frame contains the following columns:
  
       'crim'
            per capita crime rate by town.
  
       'zn'
            proportion of residential land zoned for lots over 25,000
            sq.ft.
  
       'indus'
            proportion of non-retail business acres per town.
  
       'chas'
            Charles River dummy variable (= 1 if tract bounds river; 0
            otherwise).
  
       'nox'
            nitrogen oxides concentration (parts per 10 million).
  
       'rm'
            average number of rooms per dwelling.
  
       'age'
            proportion of owner-occupied units built prior to 1940.
  
       'dis'
            weighted mean of distances to five Boston employment centres.
  
       'rad'
            index of accessibility to radial highways.
  
       'tax'
            full-value property-tax rate per $10,000.
  
       'ptratio'
            pupil-teacher ratio by town.
  
       'black'
            1000(Bk - 0.63)^2 where Bk is the proportion of blacks by
            town.
  
       'lstat'
            lower status of the population (percent).
  
       'medv'
            median value of owner-occupied homes in $1000s.
  
  _S_o_u_r_c_e:
  
       Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the
       demand for clean air.  _J. Environ. Economics and Management_ *5*,
       81-102.
  
       Belsley D.A., Kuh, E.  and Welsch, R.E. (1980) _Regression
       Diagnostics. Identifying Influential Data and Sources of
       Collinearity._ New York: Wiley.
  
  
  
  Quitting from Guide-to-SuperLearner.Rmd:557-590 [xgboost]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `FUN()`:
  ! subscript out of bounds
  ---
  Backtrace:
      ▆
   1. ├─base::system.time(...)
   2. └─SuperLearner::CV.SuperLearner(...)
   3.   └─base::lapply(cvList, "[[", "cvAllSL")
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'Guide-to-SuperLearner.Rmd' failed with diagnostics:
  subscript out of bounds
  --- failed re-building ‘Guide-to-SuperLearner.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘Guide-to-SuperLearner.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: SuperLearner
Check: tests
New result: ERROR
    Running ‘testthat.R’ [67s/63s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(SuperLearner)
    Loading required package: nnls
    Loading required package: gam
    Loading required package: splines
    Loading required package: foreach
    Loaded gam 1.22-6
    
    Super Learner
    Version: 2.0-29
    Package created on 2024-02-06
    
    > 
    > test_check("SuperLearner")
    Error in xgboost::xgboost(data = xgmat, objective = "binary:logistic",  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = "binary:logistic",  : 
      argument "y" is missing, with no default
    Error in xgboost::xgboost(data = xgmat, objective = "binary:logistic",  : 
      argument "y" is missing, with no default
    Saving _problems/test-XGBoost-25.R
    Warning: The response y is integer, bartMachine will run regression.
    Warning: The response y is integer, bartMachine will run regression.
    Warning: The response y is integer, bartMachine will run regression.
    lasso-penalized linear regression with n=506, p=13
    At minimum cross-validation error (lambda=0.0222):
    -------------------------------------------------
      Nonzero coefficients: 11
      Cross-validation error (deviance): 23.29
      R-squared: 0.72
      Signal-to-noise ratio: 2.63
      Scale estimate (sigma): 4.826
    lasso-penalized logistic regression with n=506, p=13
    At minimum cross-validation error (lambda=0.0026):
    -------------------------------------------------
      Nonzero coefficients: 12
      Cross-validation error (deviance): 0.66
      R-squared: 0.48
      Signal-to-noise ratio: 0.94
      Prediction error: 0.123
    lasso-penalized linear regression with n=506, p=13
    At minimum cross-validation error (lambda=0.0362):
    -------------------------------------------------
      Nonzero coefficients: 11
      Cross-validation error (deviance): 23.30
      R-squared: 0.72
      Signal-to-noise ratio: 2.62
      Scale estimate (sigma): 4.827
    lasso-penalized logistic regression with n=506, p=13
    At minimum cross-validation error (lambda=0.0016):
    -------------------------------------------------
      Nonzero coefficients: 13
      Cross-validation error (deviance): 0.63
      R-squared: 0.50
      Signal-to-noise ratio: 0.99
      Prediction error: 0.132
    
    Call:  
    SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",  
        "SL.biglasso"), cvControl = list(V = 2)) 
    
    
                        Risk       Coef
    SL.mean_All     84.62063 0.02136708
    SL.biglasso_All 26.01864 0.97863292
    
    Call:  
    SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
        "SL.biglasso"), cvControl = list(V = 2)) 
    
    
                         Risk Coef
    SL.mean_All     0.2346857    0
    SL.biglasso_All 0.1039122    1
    Y
     0  1 
    53 47 
    $grid
    NULL
    
    $names
    [1] "SL.randomForest_1"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    $params$ntree
    [1] 100
    
    
    [1] "SL.randomForest_1" "X"                 "Y"                
    [4] "create_rf"         "data"             
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2)) 
    
    
                              Risk Coef
    SL.randomForest_1_All 0.045984    1
    $grid
      mtry
    1    1
    2    4
    3   20
    
    $names
    [1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2)) 
    
    
                                Risk       Coef
    SL.randomForest_1_All 0.06729890 0.93195369
    SL.randomForest_2_All 0.07219426 0.00000000
    SL.randomForest_3_All 0.07243423 0.06804631
    $grid
      alpha
    1  0.00
    2  0.25
    3  0.50
    4  0.75
    5  1.00
    
    $names
    [1] "SL.glmnet_0"    "SL.glmnet_0.25" "SL.glmnet_0.5"  "SL.glmnet_0.75"
    [5] "SL.glmnet_1"   
    
    $base_learner
    [1] "SL.glmnet"
    
    $params
    list()
    
    [1] "SL.glmnet_0"    "SL.glmnet_0.25" "SL.glmnet_0.5"  "SL.glmnet_0.75"
    [5] "SL.glmnet_1"   
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),  
        cvControl = list(V = 2), env = learners) 
    
    
                             Risk Coef
    SL.glmnet_0_All    0.08849610    0
    SL.glmnet_0.25_All 0.08116755    0
    SL.glmnet_0.5_All  0.06977106    1
    SL.glmnet_0.75_All 0.07686953    0
    SL.glmnet_1_All    0.07730595    0
    
    Call:  
    SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",  
        svm$names), cvControl = list(V = 3)) 
    
    
                                Risk      Coef
    SL.mean_All           0.25711218 0.0000000
    SL.svm_polynomial_All 0.08463484 0.1443046
    SL.svm_radial_All     0.06530910 0.0000000
    SL.svm_sigmoid_All    0.05716227 0.8556954
    
    Call:  glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, 
        model = model)
    
    Coefficients:
    (Intercept)         crim           zn        indus         chas          nox  
      3.646e+01   -1.080e-01    4.642e-02    2.056e-02    2.687e+00   -1.777e+01  
             rm          age          dis          rad          tax      ptratio  
      3.810e+00    6.922e-04   -1.476e+00    3.060e-01   -1.233e-02   -9.527e-01  
          black        lstat  
      9.312e-03   -5.248e-01  
    
    Degrees of Freedom: 505 Total (i.e. Null);  492 Residual
    Null Deviance:	    42720 
    Residual Deviance: 11080 	AIC: 3028
    
    Call:
    glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, 
        model = model)
    
    Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  3.646e+01  5.103e+00   7.144 3.28e-12 ***
    crim        -1.080e-01  3.286e-02  -3.287 0.001087 ** 
    zn           4.642e-02  1.373e-02   3.382 0.000778 ***
    indus        2.056e-02  6.150e-02   0.334 0.738288    
    chas         2.687e+00  8.616e-01   3.118 0.001925 ** 
    nox         -1.777e+01  3.820e+00  -4.651 4.25e-06 ***
    rm           3.810e+00  4.179e-01   9.116  < 2e-16 ***
    age          6.922e-04  1.321e-02   0.052 0.958229    
    dis         -1.476e+00  1.995e-01  -7.398 6.01e-13 ***
    rad          3.060e-01  6.635e-02   4.613 5.07e-06 ***
    tax         -1.233e-02  3.760e-03  -3.280 0.001112 ** 
    ptratio     -9.527e-01  1.308e-01  -7.283 1.31e-12 ***
    black        9.312e-03  2.686e-03   3.467 0.000573 ***
    lstat       -5.248e-01  5.072e-02 -10.347  < 2e-16 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for gaussian family taken to be 22.51785)
    
        Null deviance: 42716  on 505  degrees of freedom
    Residual deviance: 11079  on 492  degrees of freedom
    AIC: 3027.6
    
    Number of Fisher Scoring iterations: 2
    
    
    Call:
    glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, 
        model = model)
    
    Coefficients:
                 Estimate Std. Error z value Pr(>|z|)    
    (Intercept) 10.682635   3.921395   2.724 0.006446 ** 
    crim        -0.040649   0.049796  -0.816 0.414321    
    zn           0.012134   0.010678   1.136 0.255786    
    indus       -0.040715   0.045615  -0.893 0.372078    
    chas         0.248209   0.653283   0.380 0.703989    
    nox         -3.601085   2.924365  -1.231 0.218170    
    rm           1.155157   0.374843   3.082 0.002058 ** 
    age         -0.018660   0.009319  -2.002 0.045252 *  
    dis         -0.518934   0.146286  -3.547 0.000389 ***
    rad          0.255522   0.061391   4.162 3.15e-05 ***
    tax         -0.009500   0.003107  -3.057 0.002233 ** 
    ptratio     -0.409317   0.103191  -3.967 7.29e-05 ***
    black       -0.001451   0.002558  -0.567 0.570418    
    lstat       -0.318436   0.054735  -5.818 5.96e-09 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for binomial family taken to be 1)
    
        Null deviance: 669.76  on 505  degrees of freedom
    Residual deviance: 296.39  on 492  degrees of freedom
    AIC: 324.39
    
    Number of Fisher Scoring iterations: 7
    
     [1] "coefficients"      "residuals"         "fitted.values"    
     [4] "effects"           "R"                 "rank"             
     [7] "qr"                "family"            "linear.predictors"
    [10] "deviance"          "aic"               "null.deviance"    
    [13] "iter"              "weights"           "prior.weights"    
    [16] "df.residual"       "df.null"           "y"                
    [19] "converged"         "boundary"          "call"             
    [22] "formula"           "terms"             "data"             
    [25] "offset"            "control"           "method"           
    [28] "contrasts"         "xlevels"          
    
    Call:
    glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, 
        model = model)
    
    Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  3.646e+01  5.103e+00   7.144 3.28e-12 ***
    crim        -1.080e-01  3.286e-02  -3.287 0.001087 ** 
    zn           4.642e-02  1.373e-02   3.382 0.000778 ***
    indus        2.056e-02  6.150e-02   0.334 0.738288    
    chas         2.687e+00  8.616e-01   3.118 0.001925 ** 
    nox         -1.777e+01  3.820e+00  -4.651 4.25e-06 ***
    rm           3.810e+00  4.179e-01   9.116  < 2e-16 ***
    age          6.922e-04  1.321e-02   0.052 0.958229    
    dis         -1.476e+00  1.995e-01  -7.398 6.01e-13 ***
    rad          3.060e-01  6.635e-02   4.613 5.07e-06 ***
    tax         -1.233e-02  3.760e-03  -3.280 0.001112 ** 
    ptratio     -9.527e-01  1.308e-01  -7.283 1.31e-12 ***
    black        9.312e-03  2.686e-03   3.467 0.000573 ***
    lstat       -5.248e-01  5.072e-02 -10.347  < 2e-16 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for gaussian family taken to be 22.51785)
    
        Null deviance: 42716  on 505  degrees of freedom
    Residual deviance: 11079  on 492  degrees of freedom
    AIC: 3027.6
    
    Number of Fisher Scoring iterations: 2
    
    
    Call:
    glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, 
        model = model)
    
    Coefficients:
                 Estimate Std. Error z value Pr(>|z|)    
    (Intercept) 10.682635   3.921395   2.724 0.006446 ** 
    crim        -0.040649   0.049796  -0.816 0.414321    
    zn           0.012134   0.010678   1.136 0.255786    
    indus       -0.040715   0.045615  -0.893 0.372078    
    chas         0.248209   0.653283   0.380 0.703989    
    nox         -3.601085   2.924365  -1.231 0.218170    
    rm           1.155157   0.374843   3.082 0.002058 ** 
    age         -0.018660   0.009319  -2.002 0.045252 *  
    dis         -0.518934   0.146286  -3.547 0.000389 ***
    rad          0.255522   0.061391   4.162 3.15e-05 ***
    tax         -0.009500   0.003107  -3.057 0.002233 ** 
    ptratio     -0.409317   0.103191  -3.967 7.29e-05 ***
    black       -0.001451   0.002558  -0.567 0.570418    
    lstat       -0.318436   0.054735  -5.818 5.96e-09 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for binomial family taken to be 1)
    
        Null deviance: 669.76  on 505  degrees of freedom
    Residual deviance: 296.39  on 492  degrees of freedom
    AIC: 324.39
    
    Number of Fisher Scoring iterations: 7
    
    
    Call:  
    SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",  
        "SL.glm")) 
    
    
                    Risk      Coef
    SL.mean_All 84.74142 0.0134192
    SL.glm_All  23.62549 0.9865808
           V1        
     Min.   :-3.921  
     1st Qu.:17.514  
     Median :22.124  
     Mean   :22.533  
     3rd Qu.:27.345  
     Max.   :44.376  
    
    Call:  
    SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
        "SL.glm")) 
    
    
                      Risk       Coef
    SL.mean_All 0.23580362 0.01315872
    SL.glm_All  0.09519266 0.98684128
           V1          
     Min.   :0.004942  
     1st Qu.:0.035424  
     Median :0.196222  
     Mean   :0.375494  
     3rd Qu.:0.781687  
     Max.   :0.991313  
    Got an error, as expected.
    <simpleError in predict.glmnet(object$glmnet.fit, newx, s = lambda, ...): The number of variables in newx must be 8>
    Got an error, as expected.
    <simpleError in predict.glmnet(object$glmnet.fit, newx, s = lambda, ...): The number of variables in newx must be 8>
    Call:
    lda(X, grouping = Y, prior = prior, method = method, tol = tol, 
        CV = CV, nu = nu)
    
    Prior probabilities of groups:
            0         1 
    0.6245059 0.3754941 
    
    Group means:
           crim        zn     indus       chas       nox       rm      age      dis
    0 5.2936824  4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
    1 0.8191541 22.431579  7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
            rad      tax  ptratio    black     lstat
    0 11.588608 459.9209 19.19968 340.6392 16.042468
    1  6.157895 322.2789 17.21789 383.3425  7.015947
    
    Coefficients of linear discriminants:
                      LD1
    crim     0.0012515925
    zn       0.0095179029
    indus   -0.0166376334
    chas     0.1399207112
    nox     -2.9934367740
    rm       0.5612713068
    age     -0.0128420045
    dis     -0.3095403096
    rad      0.0695027989
    tax     -0.0027771271
    ptratio -0.2059853828
    black    0.0006058031
    lstat   -0.0816668897
    Call:
    lda(X, grouping = Y, prior = prior, method = method, tol = tol, 
        CV = CV, nu = nu)
    
    Prior probabilities of groups:
            0         1 
    0.6245059 0.3754941 
    
    Group means:
           crim        zn     indus       chas       nox       rm      age      dis
    0 5.2936824  4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
    1 0.8191541 22.431579  7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
            rad      tax  ptratio    black     lstat
    0 11.588608 459.9209 19.19968 340.6392 16.042468
    1  6.157895 322.2789 17.21789 383.3425  7.015947
    
    Coefficients of linear discriminants:
                      LD1
    crim     0.0012515925
    zn       0.0095179029
    indus   -0.0166376334
    chas     0.1399207112
    nox     -2.9934367740
    rm       0.5612713068
    age     -0.0128420045
    dis     -0.3095403096
    rad      0.0695027989
    tax     -0.0027771271
    ptratio -0.2059853828
    black    0.0006058031
    lstat   -0.0816668897
    
    Call:
    stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
    
    Coefficients:
    (Intercept)         crim           zn        indus         chas          nox  
      3.646e+01   -1.080e-01    4.642e-02    2.056e-02    2.687e+00   -1.777e+01  
             rm          age          dis          rad          tax      ptratio  
      3.810e+00    6.922e-04   -1.476e+00    3.060e-01   -1.233e-02   -9.527e-01  
          black        lstat  
      9.312e-03   -5.248e-01  
    
    
    Call:
    stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
    
    Residuals:
        Min      1Q  Median      3Q     Max 
    -15.595  -2.730  -0.518   1.777  26.199 
    
    Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  3.646e+01  5.103e+00   7.144 3.28e-12 ***
    crim        -1.080e-01  3.286e-02  -3.287 0.001087 ** 
    zn           4.642e-02  1.373e-02   3.382 0.000778 ***
    indus        2.056e-02  6.150e-02   0.334 0.738288    
    chas         2.687e+00  8.616e-01   3.118 0.001925 ** 
    nox         -1.777e+01  3.820e+00  -4.651 4.25e-06 ***
    rm           3.810e+00  4.179e-01   9.116  < 2e-16 ***
    age          6.922e-04  1.321e-02   0.052 0.958229    
    dis         -1.476e+00  1.995e-01  -7.398 6.01e-13 ***
    rad          3.060e-01  6.635e-02   4.613 5.07e-06 ***
    tax         -1.233e-02  3.760e-03  -3.280 0.001112 ** 
    ptratio     -9.527e-01  1.308e-01  -7.283 1.31e-12 ***
    black        9.312e-03  2.686e-03   3.467 0.000573 ***
    lstat       -5.248e-01  5.072e-02 -10.347  < 2e-16 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Residual standard error: 4.745 on 492 degrees of freedom
    Multiple R-squared:  0.7406,	Adjusted R-squared:  0.7338 
    F-statistic: 108.1 on 13 and 492 DF,  p-value: < 2.2e-16
    
    
    Call:
    stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
    
    Residuals:
         Min       1Q   Median       3Q      Max 
    -0.80469 -0.23612 -0.03105  0.23080  1.05224 
    
    Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  1.6675402  0.3662392   4.553 6.67e-06 ***
    crim         0.0003028  0.0023585   0.128 0.897888    
    zn           0.0023028  0.0009851   2.338 0.019808 *  
    indus       -0.0040254  0.0044131  -0.912 0.362135    
    chas         0.0338534  0.0618295   0.548 0.584264    
    nox         -0.7242540  0.2741160  -2.642 0.008501 ** 
    rm           0.1357981  0.0299915   4.528 7.48e-06 ***
    age         -0.0031071  0.0009480  -3.278 0.001121 ** 
    dis         -0.0748924  0.0143135  -5.232 2.48e-07 ***
    rad          0.0168160  0.0047612   3.532 0.000451 ***
    tax         -0.0006719  0.0002699  -2.490 0.013110 *  
    ptratio     -0.0498376  0.0093885  -5.308 1.68e-07 ***
    black        0.0001466  0.0001928   0.760 0.447370    
    lstat       -0.0197591  0.0036395  -5.429 8.91e-08 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Residual standard error: 0.3405 on 492 degrees of freedom
    Multiple R-squared:  0.5192,	Adjusted R-squared:  0.5065 
    F-statistic: 40.86 on 13 and 492 DF,  p-value: < 2.2e-16
    
     [1] "coefficients"  "residuals"     "fitted.values" "effects"      
     [5] "weights"       "rank"          "assign"        "qr"           
     [9] "df.residual"   "xlevels"       "call"          "terms"        
    
    Call:
    stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
    
    Residuals:
        Min      1Q  Median      3Q     Max 
    -15.595  -2.730  -0.518   1.777  26.199 
    
    Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  3.646e+01  5.103e+00   7.144 3.28e-12 ***
    crim        -1.080e-01  3.286e-02  -3.287 0.001087 ** 
    zn           4.642e-02  1.373e-02   3.382 0.000778 ***
    indus        2.056e-02  6.150e-02   0.334 0.738288    
    chas         2.687e+00  8.616e-01   3.118 0.001925 ** 
    nox         -1.777e+01  3.820e+00  -4.651 4.25e-06 ***
    rm           3.810e+00  4.179e-01   9.116  < 2e-16 ***
    age          6.922e-04  1.321e-02   0.052 0.958229    
    dis         -1.476e+00  1.995e-01  -7.398 6.01e-13 ***
    rad          3.060e-01  6.635e-02   4.613 5.07e-06 ***
    tax         -1.233e-02  3.760e-03  -3.280 0.001112 ** 
    ptratio     -9.527e-01  1.308e-01  -7.283 1.31e-12 ***
    black        9.312e-03  2.686e-03   3.467 0.000573 ***
    lstat       -5.248e-01  5.072e-02 -10.347  < 2e-16 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Residual standard error: 4.745 on 492 degrees of freedom
    Multiple R-squared:  0.7406,	Adjusted R-squared:  0.7338 
    F-statistic: 108.1 on 13 and 492 DF,  p-value: < 2.2e-16
    
    
    Call:
    stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
    
    Residuals:
         Min       1Q   Median       3Q      Max 
    -0.80469 -0.23612 -0.03105  0.23080  1.05224 
    
    Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
    (Intercept)  1.6675402  0.3662392   4.553 6.67e-06 ***
    crim         0.0003028  0.0023585   0.128 0.897888    
    zn           0.0023028  0.0009851   2.338 0.019808 *  
    indus       -0.0040254  0.0044131  -0.912 0.362135    
    chas         0.0338534  0.0618295   0.548 0.584264    
    nox         -0.7242540  0.2741160  -2.642 0.008501 ** 
    rm           0.1357981  0.0299915   4.528 7.48e-06 ***
    age         -0.0031071  0.0009480  -3.278 0.001121 ** 
    dis         -0.0748924  0.0143135  -5.232 2.48e-07 ***
    rad          0.0168160  0.0047612   3.532 0.000451 ***
    tax         -0.0006719  0.0002699  -2.490 0.013110 *  
    ptratio     -0.0498376  0.0093885  -5.308 1.68e-07 ***
    black        0.0001466  0.0001928   0.760 0.447370    
    lstat       -0.0197591  0.0036395  -5.429 8.91e-08 ***
    ---
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Residual standard error: 0.3405 on 492 degrees of freedom
    Multiple R-squared:  0.5192,	Adjusted R-squared:  0.5065 
    F-statistic: 40.86 on 13 and 492 DF,  p-value: < 2.2e-16
    
    
    Call:  
    SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",  
        "SL.lm")) 
    
    
                   Risk       Coef
    SL.mean_All 84.6696 0.02186479
    SL.lm_All   24.3340 0.97813521
           V1        
     Min.   :-3.695  
     1st Qu.:17.557  
     Median :22.128  
     Mean   :22.533  
     3rd Qu.:27.303  
     Max.   :44.189  
    
    Call:  
    SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
        "SL.lm")) 
    
    
                     Risk Coef
    SL.mean_All 0.2349366    0
    SL.lm_All   0.1125027    1
           V1        
     Min.   :0.0000  
     1st Qu.:0.1281  
     Median :0.3530  
     Mean   :0.3899  
     3rd Qu.:0.6091  
     Max.   :1.0000  
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
        method = "method.NNLS", verbose = F, cvControl = list(V = 2)) 
    
    
                       Risk       Coef
    SL.rpart_All  0.1986827 0.31226655
    SL.glmnet_All 0.1803963 0.66105261
    SL.mean_All   0.2534500 0.02668084
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,  
        "SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2)) 
    
    
    
                              Risk       Coef
    SL.rpart_All         0.1921176 0.08939677
    SL.glmnet_All        0.1635548 0.91060323
    SL.mean_All          0.2504500 0.00000000
    SL.bad_algorithm_All        NA 0.00000000
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
        method = "method.NNLS2", verbose = F, cvControl = list(V = 2)) 
    
    
                       Risk       Coef
    SL.rpart_All  0.2279346 0.05397859
    SL.glmnet_All 0.1670620 0.94602141
    SL.mean_All   0.2504500 0.00000000
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
        method = "method.NNloglik", verbose = F, cvControl = list(V = 2)) 
    
    
                       Risk      Coef
    SL.rpart_All  0.5804469 0.1760951
    SL.glmnet_All 0.5010294 0.8239049
    SL.mean_All   0.6964542 0.0000000
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,  
        "SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2)) 
    
    
    
                              Risk      Coef
    SL.rpart_All               Inf 0.1338597
    SL.glmnet_All        0.5027498 0.8661403
    SL.mean_All          0.7000679 0.0000000
    SL.bad_algorithm_All        NA 0.0000000
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
        method = "method.CC_LS", verbose = F, cvControl = list(V = 2)) 
    
    
                       Risk       Coef
    SL.rpart_All  0.2033781 0.16438434
    SL.glmnet_All 0.1740498 0.82391928
    SL.mean_All   0.2516500 0.01169638
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
        method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2)) 
    
    
                      Risk      Coef
    SL.rpart_All  295.8455 0.1014591
    SL.glmnet_All 205.3289 0.7867610
    SL.mean_All   277.1389 0.1117798
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,  
        "SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2)) 
    
    
    
                             Risk      Coef
    SL.rpart_All         212.5569 0.2707202
    SL.glmnet_All        193.9384 0.7292798
    SL.mean_All          277.1389 0.0000000
    SL.bad_algorithm_All       NA 0.0000000
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
        method = "method.AUC", verbose = FALSE, cvControl = list(V = 2)) 
    
    
                       Risk      Coef
    SL.rpart_All  0.2533780 0.3333333
    SL.glmnet_All 0.1869683 0.3333333
    SL.mean_All   0.5550495 0.3333333
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Error in (function (Y, X, newX, ...)  : bad algorithm
    Removing failed learners: SL.bad_algorithm_All 
    Error in (function (Y, X, newX, ...)  : bad algorithm
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,  
        "SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2)) 
    
    
    
                              Risk      Coef
    SL.rpart_All         0.2467721 0.2982123
    SL.glmnet_All        0.1705535 0.3508938
    SL.mean_All          0.5150135 0.3508938
    SL.bad_algorithm_All        NA 0.0000000
    Call:
    qda(X, grouping = Y, prior = prior, method = method, tol = tol, 
        CV = CV, nu = nu)
    
    Prior probabilities of groups:
            0         1 
    0.6245059 0.3754941 
    
    Group means:
           crim        zn     indus       chas       nox       rm      age      dis
    0 5.2936824  4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
    1 0.8191541 22.431579  7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
            rad      tax  ptratio    black     lstat
    0 11.588608 459.9209 19.19968 340.6392 16.042468
    1  6.157895 322.2789 17.21789 383.3425  7.015947
    Call:
    qda(X, grouping = Y, prior = prior, method = method, tol = tol, 
        CV = CV, nu = nu)
    
    Prior probabilities of groups:
            0         1 
    0.6245059 0.3754941 
    
    Group means:
           crim        zn     indus       chas       nox       rm      age      dis
    0 5.2936824  4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
    1 0.8191541 22.431579  7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
            rad      tax  ptratio    black     lstat
    0 11.588608 459.9209 19.19968 340.6392 16.042468
    1  6.157895 322.2789 17.21789 383.3425  7.015947
    Y
     0  1 
    62 38 
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = sl_lib, cvControl = list(V = 2)) 
    
    
    
                             Risk       Coef
    SL.randomForest_All 0.0384594 0.98145221
    SL.mean_All         0.2356000 0.01854779
    $grid
    NULL
    
    $names
    [1] "SL.randomForest_1"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2)) 
    
    
                                Risk Coef
    SL.randomForest_1_All 0.05215472    1
    SL.randomForest_1 <- function(...) SL.randomForest(...) 
    $grid
    NULL
    
    $names
    [1] "SL.randomForest_1"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    [1] "SL.randomForest_1"
    [1] 1
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2), env = sl_env) 
    
    
                                Risk Coef
    SL.randomForest_1_All 0.04151372    1
    $grid
      mtry
    1    1
    2    2
    
    $names
    [1] "SL.randomForest_1" "SL.randomForest_2"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    [1] "SL.randomForest_1" "SL.randomForest_2"
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2), env = sl_env) 
    
    
                                Risk      Coef
    SL.randomForest_1_All 0.05852161 0.8484752
    SL.randomForest_2_All 0.05319324 0.1515248
    $grid
      mtry
    1    1
    2    2
    
    $names
    [1] "SL.randomForest_1" "SL.randomForest_2"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    [1] "SL.randomForest_1" "SL.randomForest_2"
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2), env = sl_env) 
    
    
                                Risk      Coef
    SL.randomForest_1_All 0.04540374 0.2120815
    SL.randomForest_2_All 0.03931360 0.7879185
    $grid
      mtry nodesize maxnodes
    1    1     NULL     NULL
    2    2     NULL     NULL
    
    $names
    [1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    [1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2), env = sl_env) 
    
    
                                          Risk      Coef
    SL.randomForest_1_NULL_NULL_All 0.05083433 0.2589592
    SL.randomForest_2_NULL_NULL_All 0.04697238 0.7410408
    $grid
      mtry maxnodes
    1    1        5
    2    2        5
    3    1       10
    4    2       10
    5    1     NULL
    6    2     NULL
    
    $names
    [1] "SL.randomForest_1_5"    "SL.randomForest_2_5"    "SL.randomForest_1_10"  
    [4] "SL.randomForest_2_10"   "SL.randomForest_1_NULL" "SL.randomForest_2_NULL"
    
    $base_learner
    [1] "SL.randomForest"
    
    $params
    list()
    
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2), env = sl_env) 
    
    
                                     Risk      Coef
    SL.randomForest_1_5_All    0.04597977 0.0000000
    SL.randomForest_2_5_All    0.03951320 0.0000000
    SL.randomForest_1_10_All   0.04337471 0.1117946
    SL.randomForest_2_10_All   0.03898477 0.8882054
    SL.randomForest_1_NULL_All 0.04395171 0.0000000
    SL.randomForest_2_NULL_All 0.03928269 0.0000000
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2)) 
    
    
                                     Risk      Coef
    SL.randomForest_1_5_All    0.05330062 0.4579034
    SL.randomForest_2_5_All    0.05189278 0.0000000
    SL.randomForest_1_10_All   0.05263432 0.1614643
    SL.randomForest_2_10_All   0.05058144 0.0000000
    SL.randomForest_1_NULL_All 0.05415397 0.0000000
    SL.randomForest_2_NULL_All 0.05036643 0.3806323
    
    Call:  
    SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
        cvControl = list(V = 2)) 
    
    
                                     Risk Coef
    SL.randomForest_1_5_All    0.05978213    0
    SL.randomForest_2_5_All    0.05628852    0
    SL.randomForest_1_10_All   0.05751494    0
    SL.randomForest_2_10_All   0.05889935    0
    SL.randomForest_1_NULL_All 0.05629605    1
    SL.randomForest_2_NULL_All 0.05807645    0
    Ranger result
    
    Call:
     ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees,      mtry = mtry, min.node.size = min.node.size, replace = replace,      sample.fraction = sample.fraction, case.weights = obsWeights,      write.forest = write.forest, probability = probability, num.threads = num.threads,      verbose = verbose) 
    
    Type:                             Regression 
    Number of trees:                  500 
    Sample size:                      506 
    Number of independent variables:  13 
    Mtry:                             3 
    Target node size:                 5 
    Variable importance mode:         none 
    Splitrule:                        variance 
    OOB prediction error (MSE):       10.57547 
    R squared (OOB):                  0.8749748 
    Ranger result
    
    Call:
     ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees,      mtry = mtry, min.node.size = min.node.size, replace = replace,      sample.fraction = sample.fraction, case.weights = obsWeights,      write.forest = write.forest, probability = probability, num.threads = num.threads,      verbose = verbose) 
    
    Type:                             Probability estimation 
    Number of trees:                  500 
    Sample size:                      506 
    Number of independent variables:  13 
    Mtry:                             3 
    Target node size:                 1 
    Variable importance mode:         none 
    Splitrule:                        gini 
    OOB prediction error (Brier s.):  0.08262419 
    Ranger result
    
    Call:
     ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees,      mtry = mtry, min.node.size = min.node.size, replace = replace,      sample.fraction = sample.fraction, case.weights = obsWeights,      write.forest = write.forest, probability = probability, num.threads = num.threads,      verbose = verbose) 
    
    Type:                             Regression 
    Number of trees:                  500 
    Sample size:                      506 
    Number of independent variables:  13 
    Mtry:                             3 
    Target node size:                 5 
    Variable importance mode:         none 
    Splitrule:                        variance 
    OOB prediction error (MSE):       10.46443 
    R squared (OOB):                  0.8762876 
    Ranger result
    
    Call:
     ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees,      mtry = mtry, min.node.size = min.node.size, replace = replace,      sample.fraction = sample.fraction, case.weights = obsWeights,      write.forest = write.forest, probability = probability, num.threads = num.threads,      verbose = verbose) 
    
    Type:                             Probability estimation 
    Number of trees:                  500 
    Sample size:                      506 
    Number of independent variables:  13 
    Mtry:                             3 
    Target node size:                 1 
    Variable importance mode:         none 
    Splitrule:                        gini 
    OOB prediction error (Brier s.):  0.08395011 
    Generalized Linear Model of class 'speedglm':
    
    Call:  speedglm::speedglm(formula = Y ~ ., data = X, family = family,      weights = obsWeights, maxit = maxit, k = k) 
    
    Coefficients:
    (Intercept)         crim           zn        indus         chas          nox  
      3.646e+01   -1.080e-01    4.642e-02    2.056e-02    2.687e+00   -1.777e+01  
             rm          age          dis          rad          tax      ptratio  
      3.810e+00    6.922e-04   -1.476e+00    3.060e-01   -1.233e-02   -9.527e-01  
          black        lstat  
      9.312e-03   -5.248e-01  
    
    Generalized Linear Model of class 'speedglm':
    
    Call:  speedglm::speedglm(formula = Y ~ ., data = X, family = family,      weights = obsWeights, maxit = maxit, k = k) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                  Estimate Std. Error  t value  Pr(>|t|)    
    (Intercept)  3.646e+01   5.103459   7.1441 3.283e-12 ***
    crim        -1.080e-01   0.032865  -3.2865 1.087e-03 ** 
    zn           4.642e-02   0.013727   3.3816 7.781e-04 ***
    indus        2.056e-02   0.061496   0.3343 7.383e-01    
    chas         2.687e+00   0.861580   3.1184 1.925e-03 ** 
    nox         -1.777e+01   3.819744  -4.6513 4.246e-06 ***
    rm           3.810e+00   0.417925   9.1161 1.979e-18 ***
    age          6.922e-04   0.013210   0.0524 9.582e-01    
    dis         -1.476e+00   0.199455  -7.3980 6.013e-13 ***
    rad          3.060e-01   0.066346   4.6129 5.071e-06 ***
    tax         -1.233e-02   0.003761  -3.2800 1.112e-03 ** 
    ptratio     -9.527e-01   0.130827  -7.2825 1.309e-12 ***
    black        9.312e-03   0.002686   3.4668 5.729e-04 ***
    lstat       -5.248e-01   0.050715 -10.3471 7.777e-23 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    
    ---
    null df: 505; null deviance: 42716.3;
    residuals df: 492; residuals deviance: 11078.78;
    # obs.: 506; # non-zero weighted obs.: 506;
    AIC: 3027.609; log Likelihood: -1498.804;
    RSS: 11078.8; dispersion: 22.51785; iterations: 1;
    rank: 14; max tolerance: 1e+00; convergence: FALSE.
    Generalized Linear Model of class 'speedglm':
    
    Call:  speedglm::speedglm(formula = Y ~ ., data = X, family = family,      weights = obsWeights, maxit = maxit, k = k) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                 Estimate Std. Error z value  Pr(>|z|)    
    (Intercept) 10.682635   3.921395  2.7242 6.446e-03 ** 
    crim        -0.040649   0.049796 -0.8163 4.143e-01    
    zn           0.012134   0.010678  1.1364 2.558e-01    
    indus       -0.040715   0.045615 -0.8926 3.721e-01    
    chas         0.248209   0.653283  0.3799 7.040e-01    
    nox         -3.601085   2.924365 -1.2314 2.182e-01    
    rm           1.155157   0.374843  3.0817 2.058e-03 ** 
    age         -0.018660   0.009319 -2.0023 4.525e-02 *  
    dis         -0.518934   0.146286 -3.5474 3.891e-04 ***
    rad          0.255522   0.061391  4.1622 3.152e-05 ***
    tax         -0.009500   0.003107 -3.0574 2.233e-03 ** 
    ptratio     -0.409317   0.103191 -3.9666 7.291e-05 ***
    black       -0.001451   0.002558 -0.5674 5.704e-01    
    lstat       -0.318436   0.054735 -5.8178 5.964e-09 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    
    ---
    null df: 505; null deviance: 669.76;
    residuals df: 492; residuals deviance: 296.39;
    # obs.: 506; # non-zero weighted obs.: 506;
    AIC: 324.3944; log Likelihood: -148.1972;
    RSS: 1107.5; dispersion: 1; iterations: 7;
    rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
    Generalized Linear Model of class 'speedglm':
    
    Call:  speedglm::speedglm(formula = Y ~ ., data = X, family = family,      weights = obsWeights, maxit = maxit, k = k) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                  Estimate Std. Error  t value  Pr(>|t|)    
    (Intercept)  3.646e+01   5.103459   7.1441 3.283e-12 ***
    crim        -1.080e-01   0.032865  -3.2865 1.087e-03 ** 
    zn           4.642e-02   0.013727   3.3816 7.781e-04 ***
    indus        2.056e-02   0.061496   0.3343 7.383e-01    
    chas         2.687e+00   0.861580   3.1184 1.925e-03 ** 
    nox         -1.777e+01   3.819744  -4.6513 4.246e-06 ***
    rm           3.810e+00   0.417925   9.1161 1.979e-18 ***
    age          6.922e-04   0.013210   0.0524 9.582e-01    
    dis         -1.476e+00   0.199455  -7.3980 6.013e-13 ***
    rad          3.060e-01   0.066346   4.6129 5.071e-06 ***
    tax         -1.233e-02   0.003761  -3.2800 1.112e-03 ** 
    ptratio     -9.527e-01   0.130827  -7.2825 1.309e-12 ***
    black        9.312e-03   0.002686   3.4668 5.729e-04 ***
    lstat       -5.248e-01   0.050715 -10.3471 7.777e-23 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    
    ---
    null df: 505; null deviance: 42716.3;
    residuals df: 492; residuals deviance: 11078.78;
    # obs.: 506; # non-zero weighted obs.: 506;
    AIC: 3027.609; log Likelihood: -1498.804;
    RSS: 11078.8; dispersion: 22.51785; iterations: 1;
    rank: 14; max tolerance: 1e+00; convergence: FALSE.
    Generalized Linear Model of class 'speedglm':
    
    Call:  speedglm::speedglm(formula = Y ~ ., data = X, family = family,      weights = obsWeights, maxit = maxit, k = k) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                 Estimate Std. Error z value  Pr(>|z|)    
    (Intercept) 10.682635   3.921395  2.7242 6.446e-03 ** 
    crim        -0.040649   0.049796 -0.8163 4.143e-01    
    zn           0.012134   0.010678  1.1364 2.558e-01    
    indus       -0.040715   0.045615 -0.8926 3.721e-01    
    chas         0.248209   0.653283  0.3799 7.040e-01    
    nox         -3.601085   2.924365 -1.2314 2.182e-01    
    rm           1.155157   0.374843  3.0817 2.058e-03 ** 
    age         -0.018660   0.009319 -2.0023 4.525e-02 *  
    dis         -0.518934   0.146286 -3.5474 3.891e-04 ***
    rad          0.255522   0.061391  4.1622 3.152e-05 ***
    tax         -0.009500   0.003107 -3.0574 2.233e-03 ** 
    ptratio     -0.409317   0.103191 -3.9666 7.291e-05 ***
    black       -0.001451   0.002558 -0.5674 5.704e-01    
    lstat       -0.318436   0.054735 -5.8178 5.964e-09 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    
    ---
    null df: 505; null deviance: 669.76;
    residuals df: 492; residuals deviance: 296.39;
    # obs.: 506; # non-zero weighted obs.: 506;
    AIC: 324.3944; log Likelihood: -148.1972;
    RSS: 1107.5; dispersion: 1; iterations: 7;
    rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
    Linear Regression Model of class 'speedlm':
    
    Call:  speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) 
    
    Coefficients:
    (Intercept)         crim           zn        indus         chas          nox  
      3.646e+01   -1.080e-01    4.642e-02    2.056e-02    2.687e+00   -1.777e+01  
             rm          age          dis          rad          tax      ptratio  
      3.810e+00    6.922e-04   -1.476e+00    3.060e-01   -1.233e-02   -9.527e-01  
          black        lstat  
      9.312e-03   -5.248e-01  
    
    Linear Regression Model of class 'speedlm':
    
    Call:  speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                      coef       se       t   p.value    
    (Intercept)  36.459488 5.103459   7.144 3.283e-12 ***
    crim         -0.108011 0.032865  -3.287 1.087e-03 ** 
    zn            0.046420 0.013727   3.382 7.781e-04 ***
    indus         0.020559 0.061496   0.334 7.383e-01    
    chas          2.686734 0.861580   3.118 1.925e-03 ** 
    nox         -17.766611 3.819744  -4.651 4.246e-06 ***
    rm            3.809865 0.417925   9.116 1.979e-18 ***
    age           0.000692 0.013210   0.052 9.582e-01    
    dis          -1.475567 0.199455  -7.398 6.013e-13 ***
    rad           0.306049 0.066346   4.613 5.071e-06 ***
    tax          -0.012335 0.003761  -3.280 1.112e-03 ** 
    ptratio      -0.952747 0.130827  -7.283 1.309e-12 ***
    black         0.009312 0.002686   3.467 5.729e-04 ***
    lstat        -0.524758 0.050715 -10.347 7.777e-23 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    ---
    Residual standard error: 4.745298 on 492 degrees of freedom;
    observations: 506;  R^2: 0.741;  adjusted R^2: 0.734;
    F-statistic: 108.1 on 13 and 492 df;  p-value: 0.
    Linear Regression Model of class 'speedlm':
    
    Call:  speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                     coef       se      t   p.value    
    (Intercept)  1.667540 0.366239  4.553 6.670e-06 ***
    crim         0.000303 0.002358  0.128 8.979e-01    
    zn           0.002303 0.000985  2.338 1.981e-02 *  
    indus       -0.004025 0.004413 -0.912 3.621e-01    
    chas         0.033853 0.061829  0.548 5.843e-01    
    nox         -0.724254 0.274116 -2.642 8.501e-03 ** 
    rm           0.135798 0.029992  4.528 7.483e-06 ***
    age         -0.003107 0.000948 -3.278 1.121e-03 ** 
    dis         -0.074892 0.014313 -5.232 2.482e-07 ***
    rad          0.016816 0.004761  3.532 4.515e-04 ***
    tax         -0.000672 0.000270 -2.490 1.311e-02 *  
    ptratio     -0.049838 0.009389 -5.308 1.677e-07 ***
    black        0.000147 0.000193  0.760 4.474e-01    
    lstat       -0.019759 0.003639 -5.429 8.912e-08 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    ---
    Residual standard error: 0.340537 on 492 degrees of freedom;
    observations: 506;  R^2: 0.519;  adjusted R^2: 0.506;
    F-statistic: 40.86 on 13 and 492 df;  p-value: 0.
    Linear Regression Model of class 'speedlm':
    
    Call:  speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                      coef       se       t   p.value    
    (Intercept)  36.459488 5.103459   7.144 3.283e-12 ***
    crim         -0.108011 0.032865  -3.287 1.087e-03 ** 
    zn            0.046420 0.013727   3.382 7.781e-04 ***
    indus         0.020559 0.061496   0.334 7.383e-01    
    chas          2.686734 0.861580   3.118 1.925e-03 ** 
    nox         -17.766611 3.819744  -4.651 4.246e-06 ***
    rm            3.809865 0.417925   9.116 1.979e-18 ***
    age           0.000692 0.013210   0.052 9.582e-01    
    dis          -1.475567 0.199455  -7.398 6.013e-13 ***
    rad           0.306049 0.066346   4.613 5.071e-06 ***
    tax          -0.012335 0.003761  -3.280 1.112e-03 ** 
    ptratio      -0.952747 0.130827  -7.283 1.309e-12 ***
    black         0.009312 0.002686   3.467 5.729e-04 ***
    lstat        -0.524758 0.050715 -10.347 7.777e-23 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    ---
    Residual standard error: 4.745298 on 492 degrees of freedom;
    observations: 506;  R^2: 0.741;  adjusted R^2: 0.734;
    F-statistic: 108.1 on 13 and 492 df;  p-value: 0.
    Linear Regression Model of class 'speedlm':
    
    Call:  speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) 
    
    Coefficients:
     ------------------------------------------------------------------ 
                     coef       se      t   p.value    
    (Intercept)  1.667540 0.366239  4.553 6.670e-06 ***
    crim         0.000303 0.002358  0.128 8.979e-01    
    zn           0.002303 0.000985  2.338 1.981e-02 *  
    indus       -0.004025 0.004413 -0.912 3.621e-01    
    chas         0.033853 0.061829  0.548 5.843e-01    
    nox         -0.724254 0.274116 -2.642 8.501e-03 ** 
    rm           0.135798 0.029992  4.528 7.483e-06 ***
    age         -0.003107 0.000948 -3.278 1.121e-03 ** 
    dis         -0.074892 0.014313 -5.232 2.482e-07 ***
    rad          0.016816 0.004761  3.532 4.515e-04 ***
    tax         -0.000672 0.000270 -2.490 1.311e-02 *  
    ptratio     -0.049838 0.009389 -5.308 1.677e-07 ***
    black        0.000147 0.000193  0.760 4.474e-01    
    lstat       -0.019759 0.003639 -5.429 8.912e-08 ***
    
    ------------------------------------------------------------------- 
    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
    ---
    Residual standard error: 0.340537 on 492 degrees of freedom;
    observations: 506;  R^2: 0.519;  adjusted R^2: 0.506;
    F-statistic: 40.86 on 13 and 492 df;  p-value: 0.
    [ FAIL 1 | WARN 34 | SKIP 9 | PASS 67 ]
    
    ══ Skipped tests (9) ═══════════════════════════════════════════════════════════
    • empty test (9): , , , , , , , ,
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-XGBoost.R:25:1'): (code run outside of `test_that()`) ──────────
    Error in `UseMethod("predict")`: no applicable method for 'predict' applied to an object of class "NULL"
    Backtrace:
        ▆
     1. ├─stats::predict(sl, X) at test-XGBoost.R:25:1
     2. └─SuperLearner::predict.SuperLearner(sl, X)
     3.   ├─base::do.call(...)
     4.   └─stats::predict(...)
    
    [ FAIL 1 | WARN 34 | SKIP 9 | PASS 67 ]
    Error:
    ! Test failures.
    Execution halted

Package: targeted
Check: examples
New result: ERROR
  Running examples in ‘targeted-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: learner_xgboost
  > ### Title: Construct a learner
  > ### Aliases: learner_xgboost
  > 
  > ### ** Examples
  > 
  > n  <- 1e3
  > x1 <- rnorm(n, sd = 2)
  > x2 <- rnorm(n)
  > lp <- x2*x1 + cos(x1)
  > yb <- rbinom(n, 1, lava::expit(lp))
  > y <-  lp + rnorm(n, sd = 0.5**.5)
  > d0 <- data.frame(y, yb, x1, x2)
  > 
  > # regression
  > lr <- learner_xgboost(y ~ x1 + x2, nrounds = 5)
  > lr$estimate(d0)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'lambda' has been renamed to 'reg_lambda'. This warning will become an error in a future version.
  Error in (function (x, y, objective = NULL, nrounds = 100L, max_depth = NULL,  : 
    argument "y" is missing, with no default
  Calls: <Anonymous> ... <Anonymous> -> process.y.margin.and.objective -> NROW
  Execution halted

Package: targeted
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘predictionclass.Rmd’ using rmarkdown
  
  Quitting from predictionclass.Rmd:143-145 [unnamed-chunk-5]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error:
  ! argument "y" is missing, with no default
  ---
  Backtrace:
      ▆
   1. └─lr_xgboost$estimate(data = pbc)
   2.   └─private$fitfun(data, ...)
   3.     ├─base::structure(do.call(private$init.estimate, args), design = summary(xx))
   4.     ├─base::do.call(private$init.estimate, args)
   5.     └─targeted (local) `<fn>`(...)
   6.       ├─base::do.call(...)
   7.       └─xgboost (local) `<fn>`(...)
   8.         └─xgboost:::process.y.margin.and.objective(...)
   9.           └─base::NROW(y)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'predictionclass.Rmd' failed with diagnostics:
  argument "y" is missing, with no default
  --- failed re-building ‘predictionclass.Rmd’
  
  --- re-building ‘riskregression.Rmd’ using rmarkdown
  --- finished re-building ‘riskregression.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘predictionclass.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: targeted
Check: tests
New result: ERROR
    Running ‘tinytest.R’ [15s/15s]
  Running the tests in ‘tests/tinytest.R’ failed.
  Complete output:
    > 
    > if (requireNamespace("tinytest", quietly = TRUE)) {
    + 
    +   ## future::plan("sequential")
    +   Sys.setenv("OMP_THREAD_LIMIT" = 2)
    +   options(Ncpus = 1)
    +   data.table::setDTthreads(1)
    + 
    +   tinytest::test_package("targeted")
    + }
    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    0 tests    
    test_cate.R...................    1 tests OK 
    test_cate.R...................    2 tests OK 
    test_cate.R...................    2 tests OK 
    test_cate.R...................   11 tests OK 
    test_cate.R...................   11 tests OK 
    test_cate.R...................   23 tests OK 
    test_cate.R...................   23 tests OK 
    test_cate.R...................   27 tests OK 
    test_cate.R...................   27 tests OK 
    test_cate.R...................   27 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   31 tests OK 
    test_cate.R...................   35 tests OK 
    test_cate.R...................   35 tests OK 
    test_cate.R...................   36 tests OK 3.5s
    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    0 tests    
    test_cumhaz.R.................    5 tests OK 
    test_cumhaz.R.................    5 tests OK 
    test_cumhaz.R.................    5 tests OK 
    test_cumhaz.R.................    5 tests OK 
    test_cumhaz.R.................    5 tests OK 
    test_cumhaz.R.................    6 tests OK 
    test_cumhaz.R.................    6 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   11 tests OK 
    test_cumhaz.R.................   12 tests OK 
    test_cumhaz.R.................   12 tests OK 
    test_cumhaz.R.................   17 tests OK 
    test_cumhaz.R.................   17 tests OK 
    test_cumhaz.R.................   22 tests OK 
    test_cumhaz.R.................   22 tests OK 
    test_cumhaz.R.................   22 tests OK 
    test_cumhaz.R.................   27 tests OK 
    test_cumhaz.R.................   27 tests OK 
    test_cumhaz.R.................   27 tests OK 
    test_cumhaz.R.................   28 tests OK 
    test_cumhaz.R.................   28 tests OK 
    test_cumhaz.R.................   28 tests OK 
    test_cumhaz.R.................   28 tests OK 
    test_cumhaz.R.................   29 tests OK 
    test_cumhaz.R.................   34 tests OK 
    test_cumhaz.R.................   34 tests OK 
    test_cumhaz.R.................   34 tests OK 
    test_cumhaz.R.................   34 tests OK 
    test_cumhaz.R.................   34 tests OK 
    test_cumhaz.R.................   35 tests OK 
    test_cumhaz.R.................   35 tests OK 
    test_cumhaz.R.................   40 tests OK 
    test_cumhaz.R.................   41 tests OK 
    test_cumhaz.R.................   41 tests OK 
    test_cumhaz.R.................   41 tests OK 
    test_cumhaz.R.................   41 tests OK 
    test_cumhaz.R.................   41 tests OK 
    test_cumhaz.R.................   42 tests OK 
    test_cumhaz.R.................   42 tests OK 
    test_cumhaz.R.................   42 tests OK 
    test_cumhaz.R.................   43 tests OK 
    test_cumhaz.R.................   43 tests OK 
    test_cumhaz.R.................   43 tests OK 
    test_cumhaz.R.................   44 tests OK 
    test_cumhaz.R.................   44 tests OK 
    test_cumhaz.R.................   44 tests OK 
    test_cumhaz.R.................   44 tests OK 
    test_cumhaz.R.................   45 tests OK 
    test_cumhaz.R.................   45 tests OK 
    test_cumhaz.R.................   46 tests OK 
    test_cumhaz.R.................   46 tests OK 
    test_cumhaz.R.................   51 tests OK 
    test_cumhaz.R.................   51 tests OK 
    test_cumhaz.R.................   51 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    Attaching package: 'survival'
    
    The following object is masked from 'package:future':
    
        cluster
    
    
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   56 tests OK 
    test_cumhaz.R.................   63 tests OK 
    test_cumhaz.R.................   63 tests OK 
    test_cumhaz.R.................   70 tests OK 
    test_cumhaz.R.................   70 tests OK 
    test_cumhaz.R.................   77 tests OK 3.7s
    
    test_design.R.................    0 tests    
    test_design.R.................    0 tests    
    test_design.R.................    0 tests    
    test_design.R.................    0 tests    
    test_design.R.................    0 tests    
    test_design.R.................   11 tests OK 
    test_design.R.................   11 tests OK 
    test_design.R.................   14 tests OK 
    test_design.R.................   14 tests OK 
    test_design.R.................   30 tests OK 
    test_design.R.................   30 tests OK 
    test_design.R.................   30 tests OK 
    test_design.R.................   36 tests OK 
    test_design.R.................   36 tests OK 
    test_design.R.................   38 tests OK 
    test_design.R.................   38 tests OK 
    test_design.R.................   48 tests OK 
    test_design.R.................   48 tests OK 
    test_design.R.................   55 tests OK 
    test_design.R.................   55 tests OK 
    test_design.R.................   62 tests OK 
    test_design.R.................   62 tests OK 
    test_design.R.................   64 tests OK 0.1s
    
    test_expand_list.R............    0 tests    
    test_expand_list.R............    0 tests    
    test_expand_list.R............    0 tests    
    test_expand_list.R............   18 tests OK 6ms
    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    
    test_intersection_sw.R........    0 tests    6ms
    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    0 tests    
    test_intsurv.R................    1 tests OK 
    test_intsurv.R................    2 tests OK 
    test_intsurv.R................    2 tests OK 
    test_intsurv.R................    2 tests OK 
    test_intsurv.R................    2 tests OK 
    test_intsurv.R................    3 tests OK 23ms
    
    test_learner.R................    0 tests    
    test_learner.R................    0 tests    
    test_learner.R................    0 tests    
    test_learner.R................    0 tests    
    test_learner.R................    0 tests    
    test_learner.R................    0 tests    
    test_learner.R................    0 tests    
    test_learner.R................    5 tests OK 
    test_learner.R................    5 tests OK 
    test_learner.R................   13 tests OK 
    test_learner.R................   13 tests OK 
    test_learner.R................   17 tests OK 
    test_learner.R................   17 tests OK 
    test_learner.R................   24 tests OK 
    test_learner.R................   24 tests OK 
    test_learner.R................   28 tests OK 
    test_learner.R................   28 tests OK 
    test_learner.R................   32 tests OK 
    test_learner.R................   32 tests OK 
    test_learner.R................   43 tests OK 
    test_learner.R................   43 tests OK 
    test_learner.R................   45 tests OK 
    test_learner.R................   45 tests OK 
    test_learner.R................   51 tests OK 0.5s
    
    test_learner_expand_grid.R....    0 tests    
    test_learner_expand_grid.R....    1 tests OK 
    test_learner_expand_grid.R....    1 tests OK 
    test_learner_expand_grid.R....    2 tests OK 
    test_learner_expand_grid.R....    2 tests OK 
    test_learner_expand_grid.R....    3 tests OK 
    test_learner_expand_grid.R....    3 tests OK 
    test_learner_expand_grid.R....    4 tests OK 
    test_learner_expand_grid.R....    4 tests OK 
    test_learner_expand_grid.R....    4 tests OK 
    test_learner_expand_grid.R....    5 tests OK 
    test_learner_expand_grid.R....    6 tests OK 
    test_learner_expand_grid.R....    6 tests OK 
    test_learner_expand_grid.R....    7 tests OK 
    test_learner_expand_grid.R....    8 tests OK 33ms
    
    test_learner_glm.R............    0 tests    
    test_learner_glm.R............    0 tests    
    test_learner_glm.R............    0 tests    
    test_learner_glm.R............    0 tests    
    test_learner_glm.R............    0 tests    
    test_learner_glm.R............    0 tests    
    test_learner_glm.R............    8 tests OK 74ms
    
    test_learner_glmnet.R.........    0 tests    
    test_learner_glmnet.R.........    0 tests    
    test_learner_glmnet.R.........    0 tests    
    test_learner_glmnet.R.........    0 tests    
    test_learner_glmnet.R.........    0 tests    
    test_learner_glmnet.R.........    0 tests    
    test_learner_glmnet.R.........    8 tests OK 1.5s
    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    0 tests    
    test_learner_grf.R............    1 tests OK 
    test_learner_grf.R............    2 tests OK 
    test_learner_grf.R............    2 tests OK 
    test_learner_grf.R............    2 tests OK 
    test_learner_grf.R............    3 tests OK 
    test_learner_grf.R............    3 tests OK 
    test_learner_grf.R............    4 tests OK 
    test_learner_grf.R............    5 tests OK 
    test_learner_grf.R............    5 tests OK 
    test_learner_grf.R............    6 tests OK 
    test_learner_grf.R............    7 tests OK 
    test_learner_grf.R............    7 tests OK 
    test_learner_grf.R............    7 tests OK 
    test_learner_grf.R............    7 tests OK 
    test_learner_grf.R............    8 tests OK 
    test_learner_grf.R............    9 tests OK 
    test_learner_grf.R............    9 tests OK 
    test_learner_grf.R............    9 tests OK 
    test_learner_grf.R............    9 tests OK 
    test_learner_grf.R............   10 tests OK 
    test_learner_grf.R............   10 tests OK 
    test_learner_grf.R............   10 tests OK 
    test_learner_grf.R............   10 tests OK 1.4s
    
    test_learner_hal.R............    0 tests    
    test_learner_hal.R............    0 tests    
    test_learner_hal.R............    0 tests    
    test_learner_hal.R............    0 tests    
    test_learner_hal.R............    3 tests OK 0.4s
    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    0 tests    
    test_learner_isoreg.R.........    1 tests OK 
    test_learner_isoreg.R.........    2 tests OK 
    test_learner_isoreg.R.........    2 tests OK 
    test_learner_isoreg.R.........    3 tests OK 9ms
    
    test_learner_mars.R...........    0 tests    
    test_learner_mars.R...........    0 tests    
    test_learner_mars.R...........    0 tests    
    test_learner_mars.R...........    0 tests    
    test_learner_mars.R...........    0 tests    
    test_learner_mars.R...........    0 tests    
    test_learner_mars.R...........    5 tests OK 0.2s
    
    test_learner_naivebayes.R.....    0 tests    
    test_learner_naivebayes.R.....    0 tests    
    test_learner_naivebayes.R.....    0 tests    
    test_learner_naivebayes.R.....    0 tests    
    test_learner_naivebayes.R.....    0 tests    
    test_learner_naivebayes.R.....    0 tests    
    test_learner_naivebayes.R.....    1 tests OK 
    test_learner_naivebayes.R.....    2 tests OK 
    test_learner_naivebayes.R.....    2 tests OK 
    test_learner_naivebayes.R.....    2 tests OK 
    test_learner_naivebayes.R.....    2 tests OK 
    test_learner_naivebayes.R.....    3 tests OK 
    test_learner_naivebayes.R.....    4 tests OK 
    test_learner_naivebayes.R.....    5 tests OK 
    test_learner_naivebayes.R.....    5 tests OK 
    test_learner_naivebayes.R.....    5 tests OK 
    test_learner_naivebayes.R.....    6 tests OK 
    test_learner_naivebayes.R.....    7 tests OK 
    test_learner_naivebayes.R.....    7 tests OK 
    test_learner_naivebayes.R.....    7 tests OK 
    test_learner_naivebayes.R.....    7 tests OK 
    test_learner_naivebayes.R.....    7 tests OK 
    test_learner_naivebayes.R.....    7 tests OK 
    test_learner_naivebayes.R.....    8 tests OK 
    test_learner_naivebayes.R.....    8 tests OK 
    test_learner_naivebayes.R.....    8 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....    9 tests OK 
    test_learner_naivebayes.R.....   10 tests OK 0.2s
    
    test_learner_sl.R.............    0 tests    
    test_learner_sl.R.............    0 tests    
    test_learner_sl.R.............    0 tests    
    test_learner_sl.R.............    0 tests    
    test_learner_sl.R.............    5 tests OK 0.5s
    
    test_learner_stratify.R.......    0 tests    
    test_learner_stratify.R.......    0 tests    
    test_learner_stratify.R.......    0 tests    
    test_learner_stratify.R.......    3 tests OK 
    test_learner_stratify.R.......    3 tests OK 
    test_learner_stratify.R.......    5 tests OK 50ms
    
    test_learner_svm.R............    0 tests    
    test_learner_svm.R............    0 tests    
    test_learner_svm.R............    0 tests    
    test_learner_svm.R............    0 tests    
    test_learner_svm.R............    0 tests    
    test_learner_svm.R............    1 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    2 tests OK 
    test_learner_svm.R............    3 tests OK 
    test_learner_svm.R............    3 tests OK 
    test_learner_svm.R............    3 tests OK 
    test_learner_svm.R............    4 tests OK 
    test_learner_svm.R............    5 tests OK 
    test_learner_svm.R............    5 tests OK 
    test_learner_svm.R............    5 tests OK 
    test_learner_svm.R............    6 tests OK 
    test_learner_svm.R............    7 tests OK 
    test_learner_svm.R............    8 tests OK 
    test_learner_svm.R............    8 tests OK 
    test_learner_svm.R............    9 tests OK 
    test_learner_svm.R............    9 tests OK 
    test_learner_svm.R............    9 tests OK 
    test_learner_svm.R............   10 tests OK 0.3s
    
    test_learner_xgboost.R........    0 tests    
    test_learner_xgboost.R........    0 tests    
    test_learner_xgboost.R........    0 tests    
    test_learner_xgboost.R........    0 tests    Error in (function (x, y, objective = NULL, nrounds = 100L, max_depth = NULL,  : 
      argument "y" is missing, with no default
    Calls: <Anonymous> ... <Anonymous> -> process.y.margin.and.objective -> NROW
    In addition: Warning messages:
    1: In throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
      Passed unrecognized parameters: verbose. This warning will become an error in a future version.
    2: In throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
      Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
    3: In throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
      Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
    4: In throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
      Parameter 'lambda' has been renamed to 'reg_lambda'. This warning will become an error in a future version.
    Execution halted

Package: tidysdm
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘a0_tidysdm_overview.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘a0_tidysdm_overview.Rmd’
  
  --- re-building ‘a1_palaeodata_application.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘a1_palaeodata_application.Rmd’
  
  --- re-building ‘a2_tidymodels_additions.Rmd’ using rmarkdown
  
  Quitting from a2_tidymodels_additions.Rmd:65-69 [vip]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `xgb.get.handle()`:
  ! 'xgb.Booster' object is corrupted or is from an incompatible XGBoost version.
  ---
  Backtrace:
       ▆
    1. ├─DALEX::model_parts(explainer = explainer_lacerta_ens)
    2. │ ├─ingredients::feature_importance(...)
    3. │ └─ingredients:::feature_importance.explainer(...)
    4. │   └─ingredients:::feature_importance.default(...)
    5. │     └─base::replicate(B, loss_after_permutation())
    6. │       └─base::sapply(...)
    7. │         └─base::lapply(X = X, FUN = FUN, ...)
    8. │           └─ingredients (local) FUN(X[[i]], ...)
    9. │             └─ingredients (local) loss_after_permutation()
   10. │               ├─DALEX (local) loss_function(observed, predict_function(x, sampled_data))
   11. │               │ └─base::tapply(observed, predicted, sum)
   12. │               └─tidysdm (local) predict_function(x, sampled_data)
   13. │                 ├─stats::predict(model, newdata)
   14. │                 └─tidysdm:::predict.simple_ensemble(model, newdata)
   15. │                   └─base::lapply(...)
   16. │                     ├─stats (local) FUN(X[[i]], ...)
   17. │                     └─workflows:::predict.workflow(X[[i]], ...)
   18. │                       ├─stats::predict(fit, new_data, type = type, opts = opts, ...)
   19. │                       └─parsnip::predict.model_fit(...)
   20. │                         ├─parsnip:::predict_classprob(...)
   21. │                         └─parsnip::predict_classprob.model_fit(...)
   22. │                           └─rlang::eval_tidy(pred_call)
   23. └─parsnip::xgb_predict(object = object$fit, new_data = new_data)
   24.   ├─stats::predict(object, new_data, ...)
   25.   └─xgboost:::predict.xgb.Booster(object, new_data, ...)
   26.     └─xgboost:::xgb.best_iteration(object)
   27.       └─xgboost::xgb.attr(bst, "best_iteration")
   28.         └─xgboost:::xgb.get.handle(object)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'a2_tidymodels_additions.Rmd' failed with diagnostics:
  'xgb.Booster' object is corrupted or is from an incompatible XGBoost version.
  --- failed re-building ‘a2_tidymodels_additions.Rmd’
  
  --- re-building ‘a3_troubleshooting.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘a3_troubleshooting.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘a2_tidymodels_additions.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: tidysdm
Check: tests
New result: ERROR
    Running ‘spelling.R’ [0s/0s]
    Running ‘testthat.R’ [65s/65s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > # This file is part of the standard setup for testthat.
    > # It is recommended that you do not modify it.
    > #
    > # Where should you do additional test configuration?
    > # Learn more about the roles of various files in:
    > # * https://r-pkgs.org/tests.html
    > # * https://testthat.r-lib.org/reference/test_package.html#special-files
    > 
    > library(testthat)
    > library(tidysdm)
    Loading required package: tidymodels
    ── Attaching packages ────────────────────────────────────── tidymodels 1.4.1 ──
    ✔ broom        1.0.10     ✔ recipes      1.3.1 
    ✔ dials        1.4.2      ✔ rsample      1.3.1 
    ✔ dplyr        1.1.4      ✔ tailor       0.1.0 
    ✔ ggplot2      4.0.1      ✔ tidyr        1.3.1 
    ✔ infer        1.0.9      ✔ tune         2.0.1 
    ✔ modeldata    1.5.1      ✔ workflows    1.3.0 
    ✔ parsnip      1.4.0      ✔ workflowsets 1.1.1 
    ✔ purrr        1.2.0      ✔ yardstick    1.3.2 
    ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
    ✖ purrr::discard() masks scales::discard()
    ✖ dplyr::filter()  masks stats::filter()
    ✖ dplyr::lag()     masks stats::lag()
    ✖ recipes::step()  masks stats::step()
    Loading required package: spatialsample
    > 
    > test_check("tidysdm")
    
    Attaching package: 'plotrix'
    
    The following object is masked from 'package:scales':
    
        rescale
    
    i Creating pre-processing data to finalize 1 unknown parameter: "mtry"
    Saving _problems/test_explain_tidysdm-121.R
    Saving _problems/test_overlap_niche-14.R
    Saving _problems/test_predict_raster-8.R
    i Creating pre-processing data to finalize 1 unknown parameter: "mtry"
    [ FAIL 3 | WARN 0 | SKIP 1 | PASS 314 ]
    
    ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
    • On CRAN (1): 'test_filter_collinear.R:2:1'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test_explain_tidysdm.R:121:3'): explain_tidysdm works with response provided directly ──
    Error in `xgb.get.handle(model)`: 'xgb.Booster' object is corrupted or is from an incompatible XGBoost version.
    Backtrace:
         ▆
      1. ├─testthat::expect_true(all.equal(test_explainer, test_explainer_y)) at test_explain_tidysdm.R:121:3
      2. │ └─testthat::quasi_label(enquo(object), label)
      3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
      4. ├─base::all.equal(test_explainer, test_explainer_y)
      5. └─base::all.equal.default(test_explainer, test_explainer_y)
      6.   └─base::all.equal.list(target, current, ...)
      7.     ├─base::all.equal(...)
      8.     └─base::all.equal.default(...)
      9.       └─base::all.equal.list(target, current, ...)
     10.         ├─base::all.equal(...)
     11.         └─base::all.equal.list(...)
     12.           ├─base::all.equal(...)
     13.           └─base::all.equal.default(...)
     14.             └─base::all.equal.list(target, current, ...)
     15.               ├─base::all.equal(...)
     16.               └─base::all.equal.default(...)
     17.                 └─base::all.equal.list(target, current, ...)
     18.                   ├─base::all.equal(...)
     19.                   └─base::all.equal.default(...)
     20.                     └─base::all.equal.list(target, current, ...)
     21.                       ├─base::all.equal(...)
     22.                       └─base::all.equal.default(...)
     23.                         └─base::all.equal.list(target, current, ...)
     24.                           └─base::attr.all.equal(target, current, ...)
     25.                             ├─base::length(target)
     26.                             └─xgboost:::length.xgb.Booster(target)
     27.                               └─xgboost::xgb.get.num.boosted.rounds(x)
     28.                                 └─xgboost:::xgb.get.handle(model)
    ── Error ('test_overlap_niche.R:14:3'): niche_overlap quantifies difference between rasters ──
    Error in `xgb.get.handle(object)`: 'xgb.Booster' object is corrupted or is from an incompatible XGBoost version.
    Backtrace:
         ▆
      1. ├─tidysdm::predict_raster(lacerta_ensemble, climate_present) at test_overlap_niche.R:14:3
      2. ├─tidysdm:::predict_raster.default(lacerta_ensemble, climate_present)
      3. │ ├─stats::predict(object, rast_sub_values, ...)
      4. │ └─tidysdm:::predict.simple_ensemble(object, rast_sub_values, ...)
      5. │   └─base::lapply(...)
      6. │     ├─stats (local) FUN(X[[i]], ...)
      7. │     └─workflows:::predict.workflow(X[[i]], ...)
      8. │       ├─stats::predict(fit, new_data, type = type, opts = opts, ...)
      9. │       └─parsnip::predict.model_fit(...)
     10. │         ├─parsnip:::predict_classprob(...)
     11. │         └─parsnip::predict_classprob.model_fit(...)
     12. │           └─rlang::eval_tidy(pred_call)
     13. └─parsnip::xgb_predict(object = object$fit, new_data = new_data)
     14.   ├─stats::predict(object, new_data, ...)
     15.   └─xgboost:::predict.xgb.Booster(object, new_data, ...)
     16.     └─xgboost:::xgb.best_iteration(object)
     17.       └─xgboost::xgb.attr(bst, "best_iteration")
     18.         └─xgboost:::xgb.get.handle(object)
    ── Error ('test_predict_raster.R:8:3'): predict_raster works correctly in chunks ──
    Error in `xgb.get.handle(object)`: 'xgb.Booster' object is corrupted or is from an incompatible XGBoost version.
    Backtrace:
         ▆
      1. ├─tidysdm::predict_raster(lacerta_ensemble, climate_future) at test_predict_raster.R:8:3
      2. ├─tidysdm:::predict_raster.default(lacerta_ensemble, climate_future)
      3. │ ├─stats::predict(object, rast_sub_values, ...)
      4. │ └─tidysdm:::predict.simple_ensemble(object, rast_sub_values, ...)
      5. │   └─base::lapply(...)
      6. │     ├─stats (local) FUN(X[[i]], ...)
      7. │     └─workflows:::predict.workflow(X[[i]], ...)
      8. │       ├─stats::predict(fit, new_data, type = type, opts = opts, ...)
      9. │       └─parsnip::predict.model_fit(...)
     10. │         ├─parsnip:::predict_classprob(...)
     11. │         └─parsnip::predict_classprob.model_fit(...)
     12. │           └─rlang::eval_tidy(pred_call)
     13. └─parsnip::xgb_predict(object = object$fit, new_data = new_data)
     14.   ├─stats::predict(object, new_data, ...)
     15.   └─xgboost:::predict.xgb.Booster(object, new_data, ...)
     16.     └─xgboost:::xgb.best_iteration(object)
     17.       └─xgboost::xgb.attr(bst, "best_iteration")
     18.         └─xgboost:::xgb.get.handle(object)
    
    [ FAIL 3 | WARN 0 | SKIP 1 | PASS 314 ]
    Error:
    ! Test failures.
    Execution halted

Package: traineR
Check: R code for possible problems
New result: NOTE
  train.xgboost: warning in xgb.train(params = params, data = train_aux,
    eval_metric = "mlogloss", nrounds = nrounds, watchlist = watchlist,
    obj = obj, feval = feval, verbose = verbose, print_every_n =
    print_every_n, early_stopping_rounds = early_stopping_rounds,
    maximize = maximize, save_period = save_period, save_name =
    save_name, xgb_model = xgb_model, callbacks = callbacks, ... = ...):
    partial argument match of 'obj' to 'objective'
  train.xgboost: warning in xgb.train(params = params, data = train_aux,
    nrounds = nrounds, watchlist = watchlist, obj = obj, feval = feval,
    verbose = verbose, print_every_n = print_every_n,
    early_stopping_rounds = early_stopping_rounds, maximize = maximize,
    save_period = save_period, save_name = save_name, xgb_model =
    xgb_model, callbacks = callbacks, ... = ...): partial argument match
    of 'obj' to 'objective'

Package: twang
Check: R code for possible problems
New result: NOTE
  ps.fast: warning in xgboost(data = sparse.data, label = data[,
    treat.var], params = params, tree_method = tree_method, feval =
    pred.xgboost, nrounds = n.trees, verbose = verbose, weight = sampW,
    callbacks = callback.list): partial argument match of 'weight' to
    'weights'

Package: vetiver
Check: tests
New result: ERROR
    Running ‘testthat.R’ [17s/17s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(vetiver)
    > 
    > test_check("vetiver")
    Loading required package: ggplot2
    Loading required package: lattice
    
    Create a Model Card for your published model
    * Model Cards provide a framework for transparent, responsible reporting
    * Use the vetiver `.Rmd` template as a place to start
    This message is displayed once per session.
    This is mgcv 1.9-4. For overview type '?mgcv'.
    
    Attaching package: 'parsnip'
    
    The following object is masked from 'package:e1071':
    
        tune
    
    
    Attaching package: 'probably'
    
    The following objects are masked from 'package:base':
    
        as.factor, as.ordered
    
    
    Attaching package: 'tune'
    
    The following object is masked from 'package:e1071':
    
        tune
    
    The following object is masked from 'package:vetiver':
    
        load_pkgs
    
    
    Attaching package: 'rsample'
    
    The following object is masked from 'package:e1071':
    
        permutations
    
    The following object is masked from 'package:caret':
    
        calibration
    
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stats':
    
        step
    
    Your rsconnect bundle has been created at:
    * /home/hornik/tmp/scratch/Rtmpk43NB2/file14949a6771092d/bundle14949a3d25fcb4.tar.gz
    Saving _problems/test-xgboost-9.R
    [ FAIL 1 | WARN 2 | SKIP 70 | PASS 221 ]
    
    ══ Skipped tests (70) ══════════════════════════════════════════════════════════
    • On CRAN (70): 'test-api.R:16:1', 'test-api.R:77:1', 'test-attach-pkgs.R:2:5',
      'test-attach-pkgs.R:7:5', 'test-attach-pkgs.R:12:5', 'test-caret.R:22:1',
      'test-caret.R:65:5', 'test-choose-version.R:4:5',
      'test-choose-version.R:33:1', 'test-create-ptype.R:41:1',
      'test-dashboard.R:13:5', 'test-gam.R:8:1', 'test-gam.R:60:5',
      'test-glm.R:7:1', 'test-glm.R:59:5', 'test-keras.R:1:1',
      'test-kproto.R:14:1', 'test-kproto.R:65:5', 'test-luz.R:1:1',
      'test-mlr3.R:3:1', 'test-mlr3.R:52:5', 'test-monitor.R:66:5',
      'test-monitor.R:72:5', 'test-monitor.R:79:5', 'test-monitor.R:124:5',
      'test-pin-read-write.R:3:1', 'test-pin-read-write.R:17:1',
      'test-pin-read-write.R:132:5', 'test-predict.R:1:1', 'test-probably.R:48:1',
      'test-probably.R:98:5', 'test-probably.R:109:1', 'test-probably.R:159:5',
      'test-probably.R:170:1', 'test-probably.R:220:5', 'test-probably.R:232:1',
      'test-probably.R:282:5', 'test-ranger.R:9:1', 'test-ranger.R:13:1',
      'test-ranger.R:62:5', 'test-recipe.R:14:1', 'test-recipe.R:58:5',
      'test-rsconnect.R:18:5', 'test-sagemaker.R:4:5', 'test-sagemaker.R:25:5',
      'test-sagemaker.R:49:1', 'test-sagemaker.R:77:1', 'test-sagemaker.R:98:1',
      'test-sagemaker.R:112:1', 'test-sagemaker.R:161:1', 'test-stacks.R:1:1',
      'test-tidymodels.R:21:1', 'test-tidymodels.R:71:5',
      'test-type-convert.R:15:1', 'test-type-convert.R:31:1',
      'test-type-convert.R:47:1', 'test-write-docker.R:5:5',
      'test-write-docker.R:17:5', 'test-write-docker.R:35:5',
      'test-write-docker.R:52:5', 'test-write-docker.R:65:5',
      'test-write-docker.R:81:5', 'test-write-docker.R:88:5',
      'test-write-plumber.R:4:5', 'test-write-plumber.R:17:5',
      'test-write-plumber.R:38:5', 'test-write-plumber.R:57:5',
      'test-write-plumber.R:71:5', 'test-write-plumber.R:84:5',
      'test-write-plumber.R:98:5'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-xgboost.R:9:1'): (code run outside of `test_that()`) ───────────
    Error in `matrix(NA_real_, ncol = model$nfeatures, dimnames = list("", model$feature_names))`: non-numeric matrix extent
    Backtrace:
        ▆
     1. └─vetiver::vetiver_model(cars_xgb, "cars2") at test-xgboost.R:9:1
     2.   └─vetiver::vetiver_create_ptype(model, save_prototype, ...)
     3.     ├─vetiver::vetiver_ptype(model, ...)
     4.     └─vetiver:::vetiver_ptype.xgb.Booster(model, ...)
     5.       └─base::matrix(...)
    
    [ FAIL 1 | WARN 2 | SKIP 70 | PASS 221 ]
    Error:
    ! Test failures.
    Execution halted

Package: VIM
Check: examples
New result: ERROR
  Running examples in ‘VIM-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: xgboostImpute
  > ### Title: Xgboost Imputation
  > ### Aliases: xgboostImpute
  > 
  > ### ** Examples
  > 
  > data(sleep)
  > xgboostImpute(Dream~BodyWgt+BrainWgt,data=sleep)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
      BodyWgt BrainWgt NonD     Dream Sleep  Span  Gest Pred Exp Danger Dream_imp
  1  6654.000  5712.00   NA 1.7996682   3.3  38.6 645.0    3   5      3      TRUE
  2     1.000     6.60  6.3 2.0000000   8.3   4.5  42.0    3   1      3     FALSE
  3     3.385    44.50   NA 3.5961127  12.5  14.0  60.0    1   1      1      TRUE
  4     0.920     5.70   NA 0.8757253  16.5    NA  25.0    5   2      3      TRUE
  5  2547.000  4603.00  2.1 1.8000000   3.9  69.0 624.0    3   5      4     FALSE
  6    10.550   179.50  9.1 0.7000000   9.8  27.0 180.0    4   4      4     FALSE
  7     0.023     0.30 15.8 3.9000000  19.7  19.0  35.0    1   1      1     FALSE
  8   160.000   169.00  5.2 1.0000000   6.2  30.4 392.0    4   5      4     FALSE
  9     3.300    25.60 10.9 3.6000000  14.5  28.0  63.0    1   2      1     FALSE
  10   52.160   440.00  8.3 1.4000000   9.7  50.0 230.0    1   1      1     FALSE
  11    0.425     6.40 11.0 1.5000000  12.5   7.0 112.0    5   4      4     FALSE
  12  465.000   423.00  3.2 0.7000000   3.9  30.0 281.0    5   5      5     FALSE
  13    0.550     2.40  7.6 2.7000000  10.3    NA    NA    2   1      2     FALSE
  14  187.100   419.00   NA 1.7214588   3.1  40.0 365.0    5   5      5      TRUE
  15    0.075     1.20  6.3 2.1000000   8.4   3.5  42.0    1   1      1     FALSE
  16    3.000    25.00  8.6 0.0000000   8.6  50.0  28.0    2   2      2     FALSE
  17    0.785     3.50  6.6 4.1000000  10.7   6.0  42.0    2   2      2     FALSE
  18    0.200     5.00  9.5 1.2000000  10.7  10.4 120.0    2   2      2     FALSE
  19    1.410    17.50  4.8 1.3000000   6.1  34.0    NA    1   2      1     FALSE
  20   60.000    81.00 12.0 6.1000000  18.1   7.0    NA    1   1      1     FALSE
  21  529.000   680.00   NA 0.3000000    NA  28.0 400.0    5   5      5     FALSE
  22   27.660   115.00  3.3 0.5000000   3.8  20.0 148.0    5   5      5     FALSE
  23    0.120     1.00 11.0 3.4000000  14.4   3.9  16.0    3   1      2     FALSE
  24  207.000   406.00   NA 1.8088160  12.0  39.3 252.0    1   4      1      TRUE
  25   85.000   325.00  4.7 1.5000000   6.2  41.0 310.0    1   3      1     FALSE
  26   36.330   119.50   NA 0.5030808  13.0  16.2  63.0    1   1      1      TRUE
  27    0.101     4.00 10.4 3.4000000  13.8   9.0  28.0    5   1      3     FALSE
  28    1.040     5.50  7.4 0.8000000   8.2   7.6  68.0    5   3      4     FALSE
  29  521.000   655.00  2.1 0.8000000   2.9  46.0 336.0    5   5      5     FALSE
  30  100.000   157.00   NA 1.0328215  10.8  22.4 100.0    1   1      1      TRUE
  31   35.000    56.00   NA 4.6171999    NA  16.3  33.0    3   5      4      TRUE
  32    0.005     0.14  7.7 1.4000000   9.1   2.6  21.5    5   2      4     FALSE
  33    0.010     0.25 17.9 2.0000000  19.9  24.0  50.0    1   1      1     FALSE
  34   62.000  1320.00  6.1 1.9000000   8.0 100.0 267.0    1   1      1     FALSE
  35    0.122     3.00  8.2 2.4000000  10.6    NA  30.0    2   1      1     FALSE
  36    1.350     8.10  8.4 2.8000000  11.2    NA  45.0    3   1      3     FALSE
  37    0.023     0.40 11.9 1.3000000  13.2   3.2  19.0    4   1      3     FALSE
  38    0.048     0.33 10.8 2.0000000  12.8   2.0  30.0    4   1      3     FALSE
  39    1.700     6.30 13.8 5.6000000  19.4   5.0  12.0    2   1      1     FALSE
  40    3.500    10.80 14.3 3.1000000  17.4   6.5 120.0    2   1      1     FALSE
  41  250.000   490.00   NA 1.0000000    NA  23.6 440.0    5   5      5     FALSE
  42    0.480    15.50 15.2 1.8000000  17.0  12.0 140.0    2   2      2     FALSE
  43   10.000   115.00 10.0 0.9000000  10.9  20.2 170.0    4   4      4     FALSE
  44    1.620    11.40 11.9 1.8000000  13.7  13.0  17.0    2   1      2     FALSE
  45  192.000   180.00  6.5 1.9000000   8.4  27.0 115.0    4   4      4     FALSE
  46    2.500    12.10  7.5 0.9000000   8.4  18.0  31.0    5   5      5     FALSE
  47    4.288    39.20   NA 2.4006271  12.5  13.7  63.0    2   2      2      TRUE
  48    0.280     1.90 10.6 2.6000000  13.2   4.7  21.0    3   1      3     FALSE
  49    4.235    50.40  7.4 2.4000000   9.8   9.8  52.0    1   1      1     FALSE
  50    6.800   179.00  8.4 1.2000000   9.6  29.0 164.0    2   3      2     FALSE
  51    0.750    12.30  5.7 0.9000000   6.6   7.0 225.0    2   2      2     FALSE
  52    3.600    21.00  4.9 0.5000000   5.4   6.0 225.0    3   2      3     FALSE
  53   14.830    98.20   NA 3.7860343   2.6  17.0 150.0    5   5      5      TRUE
  54   55.500   175.00  3.2 0.6000000   3.8  20.0 151.0    5   5      5     FALSE
  55    1.400    12.50   NA 1.0766708  11.0  12.7  90.0    2   2      2      TRUE
  56    0.060     1.00  8.1 2.2000000  10.3   3.5    NA    3   1      2     FALSE
  57    0.900     2.60 11.0 2.3000000  13.3   4.5  60.0    2   1      2     FALSE
  58    2.000    12.30  4.9 0.5000000   5.4   7.5 200.0    3   1      3     FALSE
  59    0.104     2.50 13.2 2.6000000  15.8   2.3  46.0    3   2      2     FALSE
  60    4.190    58.00  9.7 0.6000000  10.3  24.0 210.0    4   3      4     FALSE
  61    3.500     3.90 12.8 6.6000000  19.4   3.0  14.0    2   1      1     FALSE
  62    4.050    17.00   NA 0.4989381    NA  13.0  38.0    3   1      1      TRUE
  > xgboostImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
      BodyWgt BrainWgt      NonD     Dream Sleep  Span  Gest Pred Exp Danger
  1  6654.000  5712.00  2.100814 1.7996682   3.3  38.6 645.0    3   5      3
  2     1.000     6.60  6.300000 2.0000000   8.3   4.5  42.0    3   1      3
  3     3.385    44.50 10.897091 3.5961127  12.5  14.0  60.0    1   1      1
  4     0.920     5.70  7.186167 0.8757253  16.5    NA  25.0    5   2      3
  5  2547.000  4603.00  2.100000 1.8000000   3.9  69.0 624.0    3   5      4
  6    10.550   179.50  9.100000 0.7000000   9.8  27.0 180.0    4   4      4
  7     0.023     0.30 15.800000 3.9000000  19.7  19.0  35.0    1   1      1
  8   160.000   169.00  5.200000 1.0000000   6.2  30.4 392.0    4   5      4
  9     3.300    25.60 10.900000 3.6000000  14.5  28.0  63.0    1   2      1
  10   52.160   440.00  8.300000 1.4000000   9.7  50.0 230.0    1   1      1
  11    0.425     6.40 11.000000 1.5000000  12.5   7.0 112.0    5   4      4
  12  465.000   423.00  3.200000 0.7000000   3.9  30.0 281.0    5   5      5
  13    0.550     2.40  7.600000 2.7000000  10.3    NA    NA    2   1      2
  14  187.100   419.00  5.123134 1.7214588   3.1  40.0 365.0    5   5      5
  15    0.075     1.20  6.300000 2.1000000   8.4   3.5  42.0    1   1      1
  16    3.000    25.00  8.600000 0.0000000   8.6  50.0  28.0    2   2      2
  17    0.785     3.50  6.600000 4.1000000  10.7   6.0  42.0    2   2      2
  18    0.200     5.00  9.500000 1.2000000  10.7  10.4 120.0    2   2      2
  19    1.410    17.50  4.800000 1.3000000   6.1  34.0    NA    1   2      1
  20   60.000    81.00 12.000000 6.1000000  18.1   7.0    NA    1   1      1
  21  529.000   680.00  2.100814 0.3000000    NA  28.0 400.0    5   5      5
  22   27.660   115.00  3.300000 0.5000000   3.8  20.0 148.0    5   5      5
  23    0.120     1.00 11.000000 3.4000000  14.4   3.9  16.0    3   1      2
  24  207.000   406.00  6.285658 1.8088160  12.0  39.3 252.0    1   4      1
  25   85.000   325.00  4.700000 1.5000000   6.2  41.0 310.0    1   3      1
  26   36.330   119.50  3.301962 0.5030808  13.0  16.2  63.0    1   1      1
  27    0.101     4.00 10.400000 3.4000000  13.8   9.0  28.0    5   1      3
  28    1.040     5.50  7.400000 0.8000000   8.2   7.6  68.0    5   3      4
  29  521.000   655.00  2.100000 0.8000000   2.9  46.0 336.0    5   5      5
  30  100.000   157.00  4.835842 1.0328215  10.8  22.4 100.0    1   1      1
  31   35.000    56.00  9.414713 4.6171999    NA  16.3  33.0    3   5      4
  32    0.005     0.14  7.700000 1.4000000   9.1   2.6  21.5    5   2      4
  33    0.010     0.25 17.900000 2.0000000  19.9  24.0  50.0    1   1      1
  34   62.000  1320.00  6.100000 1.9000000   8.0 100.0 267.0    1   1      1
  35    0.122     3.00  8.200000 2.4000000  10.6    NA  30.0    2   1      1
  36    1.350     8.10  8.400000 2.8000000  11.2    NA  45.0    3   1      3
  37    0.023     0.40 11.900000 1.3000000  13.2   3.2  19.0    4   1      3
  38    0.048     0.33 10.800000 2.0000000  12.8   2.0  30.0    4   1      3
  39    1.700     6.30 13.800000 5.6000000  19.4   5.0  12.0    2   1      1
  40    3.500    10.80 14.300000 3.1000000  17.4   6.5 120.0    2   1      1
  41  250.000   490.00  6.742025 1.0000000    NA  23.6 440.0    5   5      5
  42    0.480    15.50 15.200000 1.8000000  17.0  12.0 140.0    2   2      2
  43   10.000   115.00 10.000000 0.9000000  10.9  20.2 170.0    4   4      4
  44    1.620    11.40 11.900000 1.8000000  13.7  13.0  17.0    2   1      2
  45  192.000   180.00  6.500000 1.9000000   8.4  27.0 115.0    4   4      4
  46    2.500    12.10  7.500000 0.9000000   8.4  18.0  31.0    5   5      5
  47    4.288    39.20  7.402267 2.4006271  12.5  13.7  63.0    2   2      2
  48    0.280     1.90 10.600000 2.6000000  13.2   4.7  21.0    3   1      3
  49    4.235    50.40  7.400000 2.4000000   9.8   9.8  52.0    1   1      1
  50    6.800   179.00  8.400000 1.2000000   9.6  29.0 164.0    2   3      2
  51    0.750    12.30  5.700000 0.9000000   6.6   7.0 225.0    2   2      2
  52    3.600    21.00  4.900000 0.5000000   5.4   6.0 225.0    3   2      3
  53   14.830    98.20 10.641701 3.7860343   2.6  17.0 150.0    5   5      5
  54   55.500   175.00  3.200000 0.6000000   3.8  20.0 151.0    5   5      5
  55    1.400    12.50  5.010159 1.0766708  11.0  12.7  90.0    2   2      2
  56    0.060     1.00  8.100000 2.2000000  10.3   3.5    NA    3   1      2
  57    0.900     2.60 11.000000 2.3000000  13.3   4.5  60.0    2   1      2
  58    2.000    12.30  4.900000 0.5000000   5.4   7.5 200.0    3   1      3
  59    0.104     2.50 13.200000 2.6000000  15.8   2.3  46.0    3   2      2
  60    4.190    58.00  9.700000 0.6000000  10.3  24.0 210.0    4   3      4
  61    3.500     3.90 12.800000 6.6000000  19.4   3.0  14.0    2   1      1
  62    4.050    17.00  5.988167 0.4989381    NA  13.0  38.0    3   1      1
     Dream_imp NonD_imp
  1       TRUE     TRUE
  2      FALSE    FALSE
  3       TRUE     TRUE
  4       TRUE     TRUE
  5      FALSE    FALSE
  6      FALSE    FALSE
  7      FALSE    FALSE
  8      FALSE    FALSE
  9      FALSE    FALSE
  10     FALSE    FALSE
  11     FALSE    FALSE
  12     FALSE    FALSE
  13     FALSE    FALSE
  14      TRUE     TRUE
  15     FALSE    FALSE
  16     FALSE    FALSE
  17     FALSE    FALSE
  18     FALSE    FALSE
  19     FALSE    FALSE
  20     FALSE    FALSE
  21     FALSE     TRUE
  22     FALSE    FALSE
  23     FALSE    FALSE
  24      TRUE     TRUE
  25     FALSE    FALSE
  26      TRUE     TRUE
  27     FALSE    FALSE
  28     FALSE    FALSE
  29     FALSE    FALSE
  30      TRUE     TRUE
  31      TRUE     TRUE
  32     FALSE    FALSE
  33     FALSE    FALSE
  34     FALSE    FALSE
  35     FALSE    FALSE
  36     FALSE    FALSE
  37     FALSE    FALSE
  38     FALSE    FALSE
  39     FALSE    FALSE
  40     FALSE    FALSE
  41     FALSE     TRUE
  42     FALSE    FALSE
  43     FALSE    FALSE
  44     FALSE    FALSE
  45     FALSE    FALSE
  46     FALSE    FALSE
  47      TRUE     TRUE
  48     FALSE    FALSE
  49     FALSE    FALSE
  50     FALSE    FALSE
  51     FALSE    FALSE
  52     FALSE    FALSE
  53      TRUE     TRUE
  54     FALSE    FALSE
  55      TRUE     TRUE
  56     FALSE    FALSE
  57     FALSE    FALSE
  58     FALSE    FALSE
  59     FALSE    FALSE
  60     FALSE    FALSE
  61     FALSE    FALSE
  62      TRUE     TRUE
  > xgboostImpute(Dream+NonD+Gest~BodyWgt+BrainWgt,data=sleep)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
      BodyWgt BrainWgt      NonD     Dream Sleep  Span      Gest Pred Exp Danger
  1  6654.000  5712.00  2.100814 1.7996682   3.3  38.6 645.00000    3   5      3
  2     1.000     6.60  6.300000 2.0000000   8.3   4.5  42.00000    3   1      3
  3     3.385    44.50 10.897091 3.5961127  12.5  14.0  60.00000    1   1      1
  4     0.920     5.70  7.186167 0.8757253  16.5    NA  25.00000    5   2      3
  5  2547.000  4603.00  2.100000 1.8000000   3.9  69.0 624.00000    3   5      4
  6    10.550   179.50  9.100000 0.7000000   9.8  27.0 180.00000    4   4      4
  7     0.023     0.30 15.800000 3.9000000  19.7  19.0  35.00000    1   1      1
  8   160.000   169.00  5.200000 1.0000000   6.2  30.4 392.00000    4   5      4
  9     3.300    25.60 10.900000 3.6000000  14.5  28.0  63.00000    1   2      1
  10   52.160   440.00  8.300000 1.4000000   9.7  50.0 230.00000    1   1      1
  11    0.425     6.40 11.000000 1.5000000  12.5   7.0 112.00000    5   4      4
  12  465.000   423.00  3.200000 0.7000000   3.9  30.0 281.00000    5   5      5
  13    0.550     2.40  7.600000 2.7000000  10.3    NA  28.07713    2   1      2
  14  187.100   419.00  5.123134 1.7214588   3.1  40.0 365.00000    5   5      5
  15    0.075     1.20  6.300000 2.1000000   8.4   3.5  42.00000    1   1      1
  16    3.000    25.00  8.600000 0.0000000   8.6  50.0  28.00000    2   2      2
  17    0.785     3.50  6.600000 4.1000000  10.7   6.0  42.00000    2   2      2
  18    0.200     5.00  9.500000 1.2000000  10.7  10.4 120.00000    2   2      2
  19    1.410    17.50  4.800000 1.3000000   6.1  34.0  80.31452    1   2      1
  20   60.000    81.00 12.000000 6.1000000  18.1   7.0 101.52599    1   1      1
  21  529.000   680.00  2.100814 0.3000000    NA  28.0 400.00000    5   5      5
  22   27.660   115.00  3.300000 0.5000000   3.8  20.0 148.00000    5   5      5
  23    0.120     1.00 11.000000 3.4000000  14.4   3.9  16.00000    3   1      2
  24  207.000   406.00  6.285658 1.8088160  12.0  39.3 252.00000    1   4      1
  25   85.000   325.00  4.700000 1.5000000   6.2  41.0 310.00000    1   3      1
  26   36.330   119.50  3.301962 0.5030808  13.0  16.2  63.00000    1   1      1
  27    0.101     4.00 10.400000 3.4000000  13.8   9.0  28.00000    5   1      3
  28    1.040     5.50  7.400000 0.8000000   8.2   7.6  68.00000    5   3      4
  29  521.000   655.00  2.100000 0.8000000   2.9  46.0 336.00000    5   5      5
  30  100.000   157.00  4.835842 1.0328215  10.8  22.4 100.00000    1   1      1
  31   35.000    56.00  9.414713 4.6171999    NA  16.3  33.00000    3   5      4
  32    0.005     0.14  7.700000 1.4000000   9.1   2.6  21.50000    5   2      4
  33    0.010     0.25 17.900000 2.0000000  19.9  24.0  50.00000    1   1      1
  34   62.000  1320.00  6.100000 1.9000000   8.0 100.0 267.00000    1   1      1
  35    0.122     3.00  8.200000 2.4000000  10.6    NA  30.00000    2   1      1
  36    1.350     8.10  8.400000 2.8000000  11.2    NA  45.00000    3   1      3
  37    0.023     0.40 11.900000 1.3000000  13.2   3.2  19.00000    4   1      3
  38    0.048     0.33 10.800000 2.0000000  12.8   2.0  30.00000    4   1      3
  39    1.700     6.30 13.800000 5.6000000  19.4   5.0  12.00000    2   1      1
  40    3.500    10.80 14.300000 3.1000000  17.4   6.5 120.00000    2   1      1
  41  250.000   490.00  6.742025 1.0000000    NA  23.6 440.00000    5   5      5
  42    0.480    15.50 15.200000 1.8000000  17.0  12.0 140.00000    2   2      2
  43   10.000   115.00 10.000000 0.9000000  10.9  20.2 170.00000    4   4      4
  44    1.620    11.40 11.900000 1.8000000  13.7  13.0  17.00000    2   1      2
  45  192.000   180.00  6.500000 1.9000000   8.4  27.0 115.00000    4   4      4
  46    2.500    12.10  7.500000 0.9000000   8.4  18.0  31.00000    5   5      5
  47    4.288    39.20  7.402267 2.4006271  12.5  13.7  63.00000    2   2      2
  48    0.280     1.90 10.600000 2.6000000  13.2   4.7  21.00000    3   1      3
  49    4.235    50.40  7.400000 2.4000000   9.8   9.8  52.00000    1   1      1
  50    6.800   179.00  8.400000 1.2000000   9.6  29.0 164.00000    2   3      2
  51    0.750    12.30  5.700000 0.9000000   6.6   7.0 225.00000    2   2      2
  52    3.600    21.00  4.900000 0.5000000   5.4   6.0 225.00000    3   2      3
  53   14.830    98.20 10.641701 3.7860343   2.6  17.0 150.00000    5   5      5
  54   55.500   175.00  3.200000 0.6000000   3.8  20.0 151.00000    5   5      5
  55    1.400    12.50  5.010159 1.0766708  11.0  12.7  90.00000    2   2      2
  56    0.060     1.00  8.100000 2.2000000  10.3   3.5  22.96904    3   1      2
  57    0.900     2.60 11.000000 2.3000000  13.3   4.5  60.00000    2   1      2
  58    2.000    12.30  4.900000 0.5000000   5.4   7.5 200.00000    3   1      3
  59    0.104     2.50 13.200000 2.6000000  15.8   2.3  46.00000    3   2      2
  60    4.190    58.00  9.700000 0.6000000  10.3  24.0 210.00000    4   3      4
  61    3.500     3.90 12.800000 6.6000000  19.4   3.0  14.00000    2   1      1
  62    4.050    17.00  5.988167 0.4989381    NA  13.0  38.00000    3   1      1
     Dream_imp NonD_imp Gest_imp
  1       TRUE     TRUE    FALSE
  2      FALSE    FALSE    FALSE
  3       TRUE     TRUE    FALSE
  4       TRUE     TRUE    FALSE
  5      FALSE    FALSE    FALSE
  6      FALSE    FALSE    FALSE
  7      FALSE    FALSE    FALSE
  8      FALSE    FALSE    FALSE
  9      FALSE    FALSE    FALSE
  10     FALSE    FALSE    FALSE
  11     FALSE    FALSE    FALSE
  12     FALSE    FALSE    FALSE
  13     FALSE    FALSE     TRUE
  14      TRUE     TRUE    FALSE
  15     FALSE    FALSE    FALSE
  16     FALSE    FALSE    FALSE
  17     FALSE    FALSE    FALSE
  18     FALSE    FALSE    FALSE
  19     FALSE    FALSE     TRUE
  20     FALSE    FALSE     TRUE
  21     FALSE     TRUE    FALSE
  22     FALSE    FALSE    FALSE
  23     FALSE    FALSE    FALSE
  24      TRUE     TRUE    FALSE
  25     FALSE    FALSE    FALSE
  26      TRUE     TRUE    FALSE
  27     FALSE    FALSE    FALSE
  28     FALSE    FALSE    FALSE
  29     FALSE    FALSE    FALSE
  30      TRUE     TRUE    FALSE
  31      TRUE     TRUE    FALSE
  32     FALSE    FALSE    FALSE
  33     FALSE    FALSE    FALSE
  34     FALSE    FALSE    FALSE
  35     FALSE    FALSE    FALSE
  36     FALSE    FALSE    FALSE
  37     FALSE    FALSE    FALSE
  38     FALSE    FALSE    FALSE
  39     FALSE    FALSE    FALSE
  40     FALSE    FALSE    FALSE
  41     FALSE     TRUE    FALSE
  42     FALSE    FALSE    FALSE
  43     FALSE    FALSE    FALSE
  44     FALSE    FALSE    FALSE
  45     FALSE    FALSE    FALSE
  46     FALSE    FALSE    FALSE
  47      TRUE     TRUE    FALSE
  48     FALSE    FALSE    FALSE
  49     FALSE    FALSE    FALSE
  50     FALSE    FALSE    FALSE
  51     FALSE    FALSE    FALSE
  52     FALSE    FALSE    FALSE
  53      TRUE     TRUE    FALSE
  54     FALSE    FALSE    FALSE
  55      TRUE     TRUE    FALSE
  56     FALSE    FALSE     TRUE
  57     FALSE    FALSE    FALSE
  58     FALSE    FALSE    FALSE
  59     FALSE    FALSE    FALSE
  60     FALSE    FALSE    FALSE
  61     FALSE    FALSE    FALSE
  62      TRUE     TRUE    FALSE
  > 
  > sleepx <- sleep
  > sleepx$Pred <- as.factor(LETTERS[sleepx$Pred])
  > sleepx$Pred[1] <- NA
  > xgboostImpute(Pred~BodyWgt+BrainWgt,data=sleepx)
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: num_class, verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Error in prescreen.objective(objective) : 
    Objectives with non-default prediction mode (reg:logistic, binary:logitraw, multi:softmax) are not supported in 'xgboost()'. Try 'xgb.train()'.
  Calls: xgboostImpute -> <Anonymous> -> prescreen.objective
  Execution halted

Package: VIM
Check: tests
New result: ERROR
    Running ‘test_imputeRobust.R’ [0s/0s]
    Running ‘tinytest.R’ [16s/16s]
  Running the tests in ‘tests/tinytest.R’ failed.
  Complete output:
    > if ( requireNamespace("tinytest", quietly=TRUE) ){
    +   tinytest::test_package("VIM")
    + }
    Loading required package: colorspace
    Loading required package: grid
    VIM is ready to use.
    
    Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
    
    Attaching package: 'VIM'
    
    The following object is masked from 'package:datasets':
    
        sleep
    
    
    test_IRMI_ordered.R...........    0 tests    
    test_IRMI_ordered.R...........    0 tests           v1        v2        co        v1        v2        co 
    -2.981434 -2.924405  2.000000  2.995740  2.483395 21.000000 
    
    test_IRMI_ordered.R...........    1 tests OK 
    test_IRMI_ordered.R...........    2 tests OK        v1        v2        co        v1        v2        co 
    -2.981434 -2.924405  2.000000  2.995740  2.483395 21.000000 
    Start:  AIC=1389.11
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - m     1   1369.1 1387.1
    - v1    1   1369.7 1387.7
    - c     4   1376.6 1388.6
    <none>      1369.1 1389.1
    - co    1   1371.2 1389.2
    - b     1   1372.2 1390.2
    - v2    1   1373.0 1391.0
    
    Step:  AIC=1387.11
    y ~ v1 + v2 + b + c + co
    
           Df Deviance    AIC
    - v1    1   1369.7 1385.7
    - c     4   1376.6 1386.6
    <none>      1369.1 1387.1
    - co    1   1371.2 1387.2
    - b     1   1372.2 1388.2
    - v2    1   1373.0 1389.0
    
    Step:  AIC=1385.69
    y ~ v2 + b + c + co
    
           Df Deviance    AIC
    - c     4   1377.1 1385.1
    <none>      1369.7 1385.7
    - co    1   1371.8 1385.8
    - b     1   1372.7 1386.7
    - v2    1   1373.7 1387.7
    
    Step:  AIC=1385.13
    y ~ v2 + b + co
    
           Df Deviance    AIC
    - co    1   1378.8 1384.8
    <none>      1377.1 1385.1
    - b     1   1380.0 1386.0
    - v2    1   1380.4 1386.4
    
    Step:  AIC=1384.77
    y ~ v2 + b
    
           Df Deviance    AIC
    <none>      1378.8 1384.8
    - v2    1   1381.8 1385.8
    - b     1   1381.9 1385.9
    Start:  AIC=1282.15
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1267.4 1279.4
    - co    1   1262.3 1280.3
    - m     1   1262.7 1280.7
    <none>      1262.2 1282.2
    - v1    1   1264.4 1282.4
    - v2    1   1305.3 1323.3
    - b     1   1320.9 1338.9
    
    Step:  AIC=1279.38
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1267.7 1277.7
    - m     1   1268.0 1278.0
    <none>      1267.4 1279.4
    - v1    1   1269.5 1279.5
    - v2    1   1308.4 1318.4
    - b     1   1325.6 1335.6
    
    Step:  AIC=1277.65
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1268.2 1276.2
    <none>      1267.7 1277.7
    - v1    1   1269.8 1277.8
    - v2    1   1309.0 1317.0
    - b     1   1325.7 1333.7
    
    Step:  AIC=1276.21
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1268.2 1276.2
    - v1    1   1270.4 1276.4
    - v2    1   1309.8 1315.8
    - b     1   1326.2 1332.2
    Start:  AIC=1279.33
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1263.5 1275.5
    - co    1   1259.5 1277.5
    - m     1   1259.8 1277.8
    <none>      1259.3 1279.3
    - v1    1   1266.6 1284.6
    - v2    1   1296.2 1314.2
    - b     1   1324.2 1342.2
    
    Step:  AIC=1275.46
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1263.7 1273.7
    - m     1   1263.9 1273.9
    <none>      1263.5 1275.5
    - v1    1   1270.5 1280.5
    - v2    1   1298.6 1308.6
    - b     1   1327.9 1337.9
    
    Step:  AIC=1273.72
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1264.2 1272.2
    <none>      1263.7 1273.7
    - v1    1   1270.8 1278.8
    - v2    1   1299.2 1307.2
    - b     1   1327.9 1335.9
    
    Step:  AIC=1272.16
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1264.2 1272.2
    - v1    1   1271.3 1277.3
    - v2    1   1299.8 1305.8
    - b     1   1328.3 1334.3
    Start:  AIC=1277.68
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1261.4 1273.4
    - co    1   1257.7 1275.7
    - m     1   1257.9 1275.9
    <none>      1257.7 1277.7
    - v1    1   1269.7 1287.7
    - v2    1   1291.2 1309.2
    - b     1   1327.5 1345.5
    
    Step:  AIC=1273.4
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1261.4 1271.4
    - m     1   1261.7 1271.7
    <none>      1261.4 1273.4
    - v1    1   1273.1 1283.1
    - v2    1   1293.3 1303.3
    - b     1   1331.0 1341.0
    
    Step:  AIC=1271.42
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1261.7 1269.7
    <none>      1261.4 1271.4
    - v1    1   1273.2 1281.2
    - v2    1   1293.4 1301.4
    - b     1   1331.1 1339.1
    
    Step:  AIC=1269.71
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1261.7 1269.7
    - v1    1   1273.5 1279.5
    - v2    1   1293.8 1299.8
    - b     1   1331.3 1337.3
    Start:  AIC=1277.6
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1260.4 1272.4
    - co    1   1257.6 1275.6
    - m     1   1258.0 1276.0
    <none>      1257.6 1277.6
    - v1    1   1272.2 1290.2
    - v2    1   1288.5 1306.5
    - b     1   1330.5 1348.5
    
    Step:  AIC=1272.41
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1260.4 1270.4
    - m     1   1260.8 1270.8
    <none>      1260.4 1272.4
    - v1    1   1274.8 1284.8
    - v2    1   1289.9 1299.9
    - b     1   1333.3 1343.3
    
    Step:  AIC=1270.41
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1260.8 1268.8
    <none>      1260.4 1270.4
    - v1    1   1274.8 1282.8
    - v2    1   1290.0 1298.0
    - b     1   1333.4 1341.4
    
    Step:  AIC=1268.83
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1260.8 1268.8
    - v1    1   1275.3 1281.3
    - v2    1   1290.6 1296.6
    - b     1   1333.7 1339.7
    
    test_IRMI_ordered.R...........    3 tests OK 
    test_IRMI_ordered.R...........    4 tests OK        v1        v2        co        v1        v2        co 
    -2.981434 -2.924405  2.000000  2.995740  2.483395 21.000000 
    Start:  AIC=1389.11
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - m     1   1369.1 1387.1
    - v1    1   1369.7 1387.7
    - c     4   1376.6 1388.6
    <none>      1369.1 1389.1
    - co    1   1371.2 1389.2
    - b     1   1372.2 1390.2
    - v2    1   1373.0 1391.0
    
    Step:  AIC=1387.11
    y ~ v1 + v2 + b + c + co
    
           Df Deviance    AIC
    - v1    1   1369.7 1385.7
    - c     4   1376.6 1386.6
    <none>      1369.1 1387.1
    - co    1   1371.2 1387.2
    - b     1   1372.2 1388.2
    - v2    1   1373.0 1389.0
    
    Step:  AIC=1385.69
    y ~ v2 + b + c + co
    
           Df Deviance    AIC
    - c     4   1377.1 1385.1
    <none>      1369.7 1385.7
    - co    1   1371.8 1385.8
    - b     1   1372.7 1386.7
    - v2    1   1373.7 1387.7
    
    Step:  AIC=1385.13
    y ~ v2 + b + co
    
           Df Deviance    AIC
    - co    1   1378.8 1384.8
    <none>      1377.1 1385.1
    - b     1   1380.0 1386.0
    - v2    1   1380.4 1386.4
    
    Step:  AIC=1384.77
    y ~ v2 + b
    
           Df Deviance    AIC
    <none>      1378.8 1384.8
    - v2    1   1381.8 1385.8
    - b     1   1381.9 1385.9
    Start:  AIC=1282.15
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1267.4 1279.4
    - co    1   1262.3 1280.3
    - m     1   1262.7 1280.7
    <none>      1262.2 1282.2
    - v1    1   1264.4 1282.4
    - v2    1   1305.3 1323.3
    - b     1   1320.9 1338.9
    
    Step:  AIC=1279.38
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1267.7 1277.7
    - m     1   1268.0 1278.0
    <none>      1267.4 1279.4
    - v1    1   1269.5 1279.5
    - v2    1   1308.4 1318.4
    - b     1   1325.6 1335.6
    
    Step:  AIC=1277.65
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1268.2 1276.2
    <none>      1267.7 1277.7
    - v1    1   1269.8 1277.8
    - v2    1   1309.0 1317.0
    - b     1   1325.7 1333.7
    
    Step:  AIC=1276.21
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1268.2 1276.2
    - v1    1   1270.4 1276.4
    - v2    1   1309.8 1315.8
    - b     1   1326.2 1332.2
    Start:  AIC=1279.33
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1263.5 1275.5
    - co    1   1259.5 1277.5
    - m     1   1259.8 1277.8
    <none>      1259.3 1279.3
    - v1    1   1266.6 1284.6
    - v2    1   1296.2 1314.2
    - b     1   1324.2 1342.2
    
    Step:  AIC=1275.46
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1263.7 1273.7
    - m     1   1263.9 1273.9
    <none>      1263.5 1275.5
    - v1    1   1270.5 1280.5
    - v2    1   1298.6 1308.6
    - b     1   1327.9 1337.9
    
    Step:  AIC=1273.72
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1264.2 1272.2
    <none>      1263.7 1273.7
    - v1    1   1270.8 1278.8
    - v2    1   1299.2 1307.2
    - b     1   1327.9 1335.9
    
    Step:  AIC=1272.16
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1264.2 1272.2
    - v1    1   1271.3 1277.3
    - v2    1   1299.8 1305.8
    - b     1   1328.3 1334.3
    Start:  AIC=1277.68
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1261.4 1273.4
    - co    1   1257.7 1275.7
    - m     1   1257.9 1275.9
    <none>      1257.7 1277.7
    - v1    1   1269.7 1287.7
    - v2    1   1291.2 1309.2
    - b     1   1327.5 1345.5
    
    Step:  AIC=1273.4
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1261.4 1271.4
    - m     1   1261.7 1271.7
    <none>      1261.4 1273.4
    - v1    1   1273.1 1283.1
    - v2    1   1293.3 1303.3
    - b     1   1331.0 1341.0
    
    Step:  AIC=1271.42
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1261.7 1269.7
    <none>      1261.4 1271.4
    - v1    1   1273.2 1281.2
    - v2    1   1293.4 1301.4
    - b     1   1331.1 1339.1
    
    Step:  AIC=1269.71
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1261.7 1269.7
    - v1    1   1273.5 1279.5
    - v2    1   1293.8 1299.8
    - b     1   1331.3 1337.3
    Start:  AIC=1277.6
    y ~ v1 + v2 + m + b + c + co
    
           Df Deviance    AIC
    - c     4   1260.4 1272.4
    - co    1   1257.6 1275.6
    - m     1   1258.0 1276.0
    <none>      1257.6 1277.6
    - v1    1   1272.2 1290.2
    - v2    1   1288.5 1306.5
    - b     1   1330.5 1348.5
    
    Step:  AIC=1272.41
    y ~ v1 + v2 + m + b + co
    
           Df Deviance    AIC
    - co    1   1260.4 1270.4
    - m     1   1260.8 1270.8
    <none>      1260.4 1272.4
    - v1    1   1274.8 1284.8
    - v2    1   1289.9 1299.9
    - b     1   1333.3 1343.3
    
    Step:  AIC=1270.41
    y ~ v1 + v2 + m + b
    
           Df Deviance    AIC
    - m     1   1260.8 1268.8
    <none>      1260.4 1270.4
    - v1    1   1274.8 1282.8
    - v2    1   1290.0 1298.0
    - b     1   1333.4 1341.4
    
    Step:  AIC=1268.83
    y ~ v1 + v2 + b
    
           Df Deviance    AIC
    <none>      1260.8 1268.8
    - v1    1   1275.3 1281.3
    - v2    1   1290.6 1296.6
    - b     1   1333.7 1339.7
    
    test_IRMI_ordered.R...........    5 tests OK 
    test_IRMI_ordered.R...........    6 tests OK        v1        v2        co        v1        v2        co 
    -2.981434 -2.924405  2.000000  2.995740  2.483395 21.000000 
    
    test_IRMI_ordered.R...........    7 tests OK 
    test_IRMI_ordered.R...........    8 tests OK 
    test_IRMI_ordered.R...........    9 tests OK        v1        v2        co        v1        v2        co 
    -2.981434 -2.924405  2.000000  2.995740  2.483395 21.000000 
    
    test_IRMI_ordered.R...........   10 tests OK 
    test_IRMI_ordered.R...........   11 tests OK        v1        v2        co        v1        v2        co 
    -2.981434 -2.924405  2.000000  2.995740  2.483395 21.000000 
    
    test_IRMI_ordered.R...........   12 tests OK 
    test_IRMI_ordered.R...........   13 tests OK 4.4s
    
    test_aggFunctions.R...........    0 tests    kNN ordered
    
    test_aggFunctions.R...........    0 tests    
    test_aggFunctions.R...........    0 tests    
    test_aggFunctions.R...........    0 tests    
    test_aggFunctions.R...........    0 tests    
    test_aggFunctions.R...........    0 tests    
    test_aggFunctions.R...........    1 tests OK 
    test_aggFunctions.R...........    2 tests OK 
    test_aggFunctions.R...........    3 tests OK 
    test_aggFunctions.R...........    4 tests OK 
    test_aggFunctions.R...........    5 tests OK 
    test_aggFunctions.R...........    6 tests OK 9ms
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
        filter, lag
    
    The following objects are masked from 'package:base':
    
        intersect, setdiff, setequal, union
    
    
    test_data_frame.R.............    0 tests    
    test_data_frame.R.............    0 tests    b c b c 
    1 1 5 4 
    a c a c 
    1 1 5 4 
    a b a b 
    1 1 5 5 
    
    test_data_frame.R.............    0 tests    b c b c 
    1 1 5 4 
    a c a c 
    1 1 5 4 
    a b a b 
    1 1 5 5 
    
    test_data_frame.R.............    0 tests    
    test_data_frame.R.............    1 tests OK 
    test_data_frame.R.............    1 tests OK 
    test_data_frame.R.............    1 tests OK 
    test_data_frame.R.............    2 tests OK b c b c 
    1 1 5 4 
    a c a c 
    1 1 5 4 
    a b a b 
    1 1 5 5 
    
    test_data_frame.R.............    2 tests OK b c b c 
    1 1 5 4 
    a c a c 
    1 1 5 4 
    a b a b 
    1 1 5 5 
    
    test_data_frame.R.............    2 tests OK 
    test_data_frame.R.............    3 tests OK 0.8s
    
    test_gowerDind.R..............    0 tests    
    test_gowerDind.R..............    0 tests            x         y         x         y 
    -2.602266 -3.387941  2.194409  2.987243 
    
    test_gowerDind.R..............    0 tests    
    test_gowerDind.R..............    0 tests            x         y         x         y 
    -2.602266 -3.387941  2.194409  2.987243 
    
    test_gowerDind.R..............    0 tests    
    test_gowerDind.R..............    0 tests    
    test_gowerDind.R..............    1 tests OK 
    test_gowerDind.R..............    1 tests OK 
    test_gowerDind.R..............    1 tests OK 
    test_gowerDind.R..............    1 tests OK 
    test_gowerDind.R..............    1 tests OK 
    test_gowerDind.R..............    2 tests OK 61ms
    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    0 tests    
     Missings in variables:
     Variable Count
         NonD    14
        Dream    12
        Sleep     4
         Span     4
         Gest     4
    
    test_graphics.R...............    0 tests    
    test_graphics.R...............    1 tests OK  BodyWgt BrainWgt    Dream    Sleep     Span     Gest     Pred      Exp 
       0.005    0.140    0.000    2.600    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt    Dream    Sleep     Span     Gest     Pred 
       1.000 6654.000 5712.000    6.600   19.900  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Sleep     Span     Gest     Pred      Exp 
       0.005    0.140    2.100    2.600    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Sleep     Span     Gest     Pred 
       1.000 6654.000 5712.000   17.900   19.900  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream     Span     Gest     Pred      Exp 
       0.005    0.140    2.100    0.000    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream     Span     Gest     Pred 
       1.000 6654.000 5712.000   17.900    6.600  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Gest     Pred      Exp 
       0.005    0.140    2.100    0.000    2.600   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream    Sleep     Gest     Pred 
       1.000 6654.000 5712.000   17.900    6.600   19.900  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Span     Pred      Exp 
       0.005    0.140    2.100    0.000    2.600    2.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream    Sleep     Span     Pred 
       1.000 6654.000 5712.000   17.900    6.600   19.900  100.000    5.000 
         Exp   Danger 
       5.000    5.000 
    
    test_graphics.R...............    1 tests OK 
    test_graphics.R...............    1 tests OK 
    test_graphics.R...............    1 tests OK 
     Imputed missings in variables:
     Variable Count
         NonD    14
        Dream    12
        Sleep     4
         Span     4
         Gest     4
    
    test_graphics.R...............    1 tests OK 
    test_graphics.R...............    2 tests OK 
    test_graphics.R...............    2 tests OK 
    test_graphics.R...............    2 tests OK 
    test_graphics.R...............    3 tests OK Exp Exp 
      1   5 
    
    test_graphics.R...............    3 tests OK 
    test_graphics.R...............    3 tests OK 
    test_graphics.R...............    3 tests OK 
    test_graphics.R...............    4 tests OK 
    test_graphics.R...............    4 tests OK 
    test_graphics.R...............    5 tests OK       Ca       Bi       Ca       Bi 
    1.10e+02 6.00e-03 4.17e+04 3.89e+00 
         Ca      As      Ca      As 
      110.0     0.1 41700.0    30.7 
    
    test_graphics.R...............    5 tests OK 
    test_graphics.R...............    6 tests OK 
    test_graphics.R...............    6 tests OK 
    test_graphics.R...............    6 tests OK 
    test_graphics.R...............    6 tests OK 
    test_graphics.R...............    7 tests OK Humidity Humidity 
        71.6     94.8 
    Air.Temp Air.Temp 
       21.42    28.50 
    
    test_graphics.R...............    7 tests OK 
    test_graphics.R...............    7 tests OK 
    test_graphics.R...............    7 tests OK 
    test_graphics.R...............    8 tests OK 
    test_graphics.R...............    8 tests OK 
    test_graphics.R...............    9 tests OK    Bi    Bi 
    0.006 3.890 
      As   As 
     0.1 30.7 
    
    test_graphics.R...............    9 tests OK 
    test_graphics.R...............   10 tests OK 
    test_graphics.R...............   10 tests OK 
    test_graphics.R...............   10 tests OK 
    test_graphics.R...............   11 tests OK  BodyWgt BrainWgt    Dream    Sleep  BodyWgt BrainWgt    Dream    Sleep 
       0.005    0.140    0.000    2.600 6654.000 5712.000    6.600   19.900 
     BodyWgt BrainWgt     NonD    Sleep  BodyWgt BrainWgt     NonD    Sleep 
       0.005    0.140    2.100    2.600 6654.000 5712.000   17.900   19.900 
     BodyWgt BrainWgt     NonD    Dream  BodyWgt BrainWgt     NonD    Dream 
       0.005    0.140    2.100    0.000 6654.000 5712.000   17.900    6.600 
    
    test_graphics.R...............   11 tests OK 
    test_graphics.R...............   11 tests OK 
    test_graphics.R...............   12 tests OK 
    test_graphics.R...............   12 tests OK 
    test_graphics.R...............   12 tests OK 
    test_graphics.R...............   13 tests OK  BodyWgt BrainWgt    Dream    Sleep     Span     Gest  BodyWgt BrainWgt 
       0.005    0.140    0.000    2.600    2.000   12.000 6654.000 5712.000 
       Dream    Sleep     Span     Gest 
       6.600   19.900  100.000  645.000 
     BodyWgt BrainWgt     NonD    Sleep     Span     Gest  BodyWgt BrainWgt 
       0.005    0.140    2.100    2.600    2.000   12.000 6654.000 5712.000 
        NonD    Sleep     Span     Gest 
      17.900   19.900  100.000  645.000 
     BodyWgt BrainWgt     NonD    Dream     Span     Gest  BodyWgt BrainWgt 
       0.005    0.140    2.100    0.000    2.000   12.000 6654.000 5712.000 
        NonD    Dream     Span     Gest 
      17.900    6.600  100.000  645.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Gest  BodyWgt BrainWgt 
       0.005    0.140    2.100    0.000    2.600   12.000 6654.000 5712.000 
        NonD    Dream    Sleep     Gest 
      17.900    6.600   19.900  645.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Span  BodyWgt BrainWgt 
       0.005    0.140    2.100    0.000    2.600    2.000 6654.000 5712.000 
        NonD    Dream    Sleep     Span 
      17.900    6.600   19.900  100.000 
    
    test_graphics.R...............   13 tests OK 
    test_graphics.R...............   13 tests OK 
    test_graphics.R...............   14 tests OK 
    test_graphics.R...............   14 tests OK 
    test_graphics.R...............   15 tests OK  BodyWgt BrainWgt    Dream    Sleep     Span     Gest     Pred      Exp 
       0.005    0.140    0.000    2.600    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt    Dream    Sleep     Span     Gest     Pred 
       1.000 6654.000 5712.000    6.600   19.900  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Sleep     Span     Gest     Pred      Exp 
       0.005    0.140    2.100    2.600    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Sleep     Span     Gest     Pred 
       1.000 6654.000 5712.000   17.900   19.900  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream     Span     Gest     Pred      Exp 
       0.005    0.140    2.100    0.000    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream     Span     Gest     Pred 
       1.000 6654.000 5712.000   17.900    6.600  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Gest     Pred      Exp 
       0.005    0.140    2.100    0.000    2.600   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream    Sleep     Gest     Pred 
       1.000 6654.000 5712.000   17.900    6.600   19.900  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Span     Pred      Exp 
       0.005    0.140    2.100    0.000    2.600    2.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream    Sleep     Span     Pred 
       1.000 6654.000 5712.000   17.900    6.600   19.900  100.000    5.000 
         Exp   Danger 
       5.000    5.000 
    
    test_graphics.R...............   15 tests OK 
    test_graphics.R...............   16 tests OK 
    test_graphics.R...............   17 tests OK Al_XRF Ca_XRF Fe_XRF  K_XRF Mg_XRF Mn_XRF Na_XRF  P_XRF Si_XRF Ti_XRF Al_XRF 
     2.920  0.030  0.590  0.360  0.120  0.015  0.080  0.004 17.050  0.053 12.080 
    Ca_XRF Fe_XRF  K_XRF Mg_XRF Mn_XRF Na_XRF  P_XRF Si_XRF Ti_XRF 
     6.760 12.350  5.240  7.320  0.356  4.870  0.589 40.270  1.900 
    
    test_graphics.R...............   17 tests OK 
    test_graphics.R...............   18 tests OK 
    test_graphics.R...............   19 tests OK Humidity Humidity 
        71.6     94.8 
    Air.Temp Air.Temp 
       21.42    28.50 
    
    test_graphics.R...............   20 tests OK 
    test_graphics.R...............   21 tests OK Humidity Humidity 
        71.6     94.8 
    Air.Temp Air.Temp 
       21.42    28.50 
    
    test_graphics.R...............   22 tests OK 
    test_graphics.R...............   22 tests OK 
    test_graphics.R...............   22 tests OK 
    test_graphics.R...............   23 tests OK  BodyWgt BrainWgt    Dream    Sleep  BodyWgt BrainWgt    Dream    Sleep 
       0.005    0.140    0.000    2.600 6654.000 5712.000    6.600   19.900 
     BodyWgt BrainWgt     NonD    Sleep  BodyWgt BrainWgt     NonD    Sleep 
       0.005    0.140    2.100    2.600 6654.000 5712.000   17.900   19.900 
     BodyWgt BrainWgt     NonD    Dream  BodyWgt BrainWgt     NonD    Dream 
       0.005    0.140    2.100    0.000 6654.000 5712.000   17.900    6.600 
    
    test_graphics.R...............   23 tests OK 
    test_graphics.R...............   23 tests OK 
    test_graphics.R...............   24 tests OK 
    test_graphics.R...............   24 tests OK 
    test_graphics.R...............   24 tests OK 
    test_graphics.R...............   25 tests OK Humidity Humidity 
        71.6     94.8 
    Air.Temp Air.Temp 
       21.42    28.50 
    
    test_graphics.R...............   25 tests OK Exp Exp 
      1   5 
    
    test_graphics.R...............   25 tests OK 
    test_graphics.R...............   26 tests OK 
    test_graphics.R...............   27 tests OK 
    test_graphics.R...............   28 tests OK Humidity Humidity 
        71.6     94.8 
    Air.Temp Air.Temp 
       21.42    28.50 
    
    test_graphics.R...............   29 tests OK        CaO        CaO 
    -1.3010300  0.9758911 
    
    test_graphics.R...............   30 tests OK 
    test_graphics.R...............   30 tests OK 
    test_graphics.R...............   31 tests OK 
    test_graphics.R...............   31 tests OK 
    test_graphics.R...............   32 tests OK 5.3s
    hotdeck
    
    test_hotdeck.R................    0 tests    
    Attaching package: 'data.table'
    
    The following objects are masked from 'package:dplyr':
    
        between, first, last
    
    
    test_hotdeck.R................    0 tests    
    test_hotdeck.R................    0 tests    
    test_hotdeck.R................    0 tests    
    test_hotdeck.R................    0 tests    
    test_hotdeck.R................    0 tests    
    test_hotdeck.R................    1 tests OK 
    test_hotdeck.R................    1 tests OK 
    test_hotdeck.R................    1 tests OK 
    test_hotdeck.R................    2 tests OK 
    test_hotdeck.R................    2 tests OK 
    test_hotdeck.R................    3 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    4 tests OK 
    test_hotdeck.R................    5 tests OK 
    test_hotdeck.R................    5 tests OK 
    test_hotdeck.R................    6 tests OK 
    test_hotdeck.R................    6 tests OK 
    test_hotdeck.R................    7 tests OK 
    test_hotdeck.R................    7 tests OK 
    test_hotdeck.R................    8 tests OK 
    test_hotdeck.R................    8 tests OK 
    test_hotdeck.R................    9 tests OK 
    test_hotdeck.R................   10 tests OK 
    test_hotdeck.R................   10 tests OK 
    test_hotdeck.R................   10 tests OK 
    test_hotdeck.R................   11 tests OK 
    test_hotdeck.R................   12 tests OK 
    test_hotdeck.R................   13 tests OK 
    test_hotdeck.R................   14 tests OK 
    test_hotdeck.R................   15 tests OK 
    test_hotdeck.R................   15 tests OK 
    test_hotdeck.R................   15 tests OK 
    test_hotdeck.R................   16 tests OK 
    test_hotdeck.R................   17 tests OK 
    test_hotdeck.R................   18 tests OK 
    test_hotdeck.R................   19 tests OK 0.5s
    
    test_impNA.R..................    0 tests    
    test_impNA.R..................    0 tests    
    test_impNA.R..................    0 tests    
    test_impNA.R..................    0 tests     BodyWgt BrainWgt    Dream    Sleep     Span     Gest     Pred      Exp 
       0.005    0.140    0.000    2.900    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt    Dream    Sleep     Span     Gest     Pred 
       1.000 6654.000 5712.000    6.600   19.900  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Sleep     Span     Gest     Pred      Exp 
       0.005    0.140    2.100    2.900    2.000   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Sleep     Span     Gest     Pred 
       1.000 6654.000 5712.000   17.900   19.900  100.000  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
     BodyWgt BrainWgt     NonD    Dream    Sleep     Gest     Pred      Exp 
       0.005    0.140    2.100    0.000    2.600   12.000    1.000    1.000 
      Danger  BodyWgt BrainWgt     NonD    Dream    Sleep     Gest     Pred 
       1.000 6654.000 5712.000   17.900    6.600   19.900  645.000    5.000 
         Exp   Danger 
       5.000    5.000 
    
    test_impNA.R..................    0 tests    
    test_impNA.R..................    1 tests OK 
    test_impNA.R..................    2 tests OK 
    test_impNA.R..................    2 tests OK 
    test_impNA.R..................    3 tests OK 
    test_impNA.R..................    4 tests OK 0.2s
    impPCA
    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    0 tests    
    Iterations: 4
    
    test_impPCA.R.................    0 tests    
    test_impPCA.R.................    1 tests OK 
    Iterations: 4
    
    test_impPCA.R.................    1 tests OK 
    test_impPCA.R.................    2 tests OK 
    Iterations: 0
    
    test_impPCA.R.................    2 tests OK 
    test_impPCA.R.................    3 tests OK 
    Iterations: 0
    
    test_impPCA.R.................    3 tests OK 
    test_impPCA.R.................    4 tests OK 84ms
    
    test_irmi_types.R.............    0 tests             z          z 
    -0.3308959  2.0121804 
    
    test_irmi_types.R.............    0 tests    
    test_irmi_types.R.............    1 tests OK 
    test_irmi_types.R.............    2 tests OK 
    test_irmi_types.R.............    2 tests OK 
    test_irmi_types.R.............    2 tests OK 
    test_irmi_types.R.............    2 tests OK 
    test_irmi_types.R.............    2 tests OK 
    test_irmi_types.R.............    3 tests OK 
    test_irmi_types.R.............    4 tests OK 
    test_irmi_types.R.............    4 tests OK 
    test_irmi_types.R.............    4 tests OK 
    test_irmi_types.R.............    4 tests OK 
    test_irmi_types.R.............    5 tests OK 
    test_irmi_types.R.............    6 tests OK 
    test_irmi_types.R.............    6 tests OK 
    test_irmi_types.R.............    6 tests OK      num1      num2      num3      num1      num2      num3 
    -3.087610 -4.001394 -3.237928  3.349508  3.615635  2.820386 
    
    test_irmi_types.R.............    6 tests OK 
    test_irmi_types.R.............    7 tests OK 
    test_irmi_types.R.............    8 tests OK 0.7s
    
    test_kNN.R....................    0 tests    kNN general
    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    y y 
    1 6 
    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    y y 
    1 6 
    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    0 tests    
    test_kNN.R....................    1 tests OK 
    test_kNN.R....................    1 tests OK 
    test_kNN.R....................    1 tests OK Detected as categorical variable:
    
    x,x_imp,y_imp
    Detected as ordinal variable:
    
    
    Detected as numerical variable:
    
    y
    0 items ofvariable:x imputed
    
    6items ofvariable:y imputed
    
    Time difference of 0.01680303 secs
    
    test_kNN.R....................    1 tests OK 
    test_kNN.R....................    2 tests OK 
    test_kNN.R....................    2 tests OK 
    test_kNN.R....................    2 tests OK 
    test_kNN.R....................    2 tests OK                           y                           z 
                       1.000000                    1.000000 
    RandomVariableForImputation                           y 
                      -1.372898                    6.000000 
                              z RandomVariableForImputation 
                       6.000000                    2.212962 
                              z RandomVariableForImputation 
                       1.000000                   -1.372898 
                              z RandomVariableForImputation 
                       6.000000                    2.212962 
                              y                           z 
                       1.000000                    1.000000 
    RandomVariableForImputation                           y 
                      -1.372898                    6.000000 
                              z RandomVariableForImputation 
                       6.000000                    2.212962 
    
    test_kNN.R....................    2 tests OK 
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    3 tests OK                           y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.5949014                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              z                          y2 
                      1.0000000                   1.0000000 
                             z2                          m2 
                      1.0000000                  -0.2369185 
                            y23                         z23 
                      1.0000000                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.5949014 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.5949014                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.5949014                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             z2                          m2 
                      1.0000000                  -0.2369185 
                            y23                         z23 
                      1.0000000                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.5949014 
                              y                           z 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                            y23                         z23 
                      1.0000000                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.5949014 
                              y                           z 
                      6.0000000                   6.0000000 
                             y2                          z2 
                      6.0000000                   6.0000000 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.5949014                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         z23 
                     -0.2369185                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.5949014 
                              y                           z 
                      6.0000000                   6.0000000 
                             y2                          z2 
                      6.0000000                   6.0000000 
                             m2                         z23 
                      1.0393184                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   2.0511976 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23 RandomVariableForImputation 
                      1.0000000                  -1.5949014 
                              y                           z 
                      6.0000000                   6.0000000 
                             y2                          z2 
                      6.0000000                   6.0000000 
                             m2                         y23 
                      1.0393184                   6.0000000 
                            z23 RandomVariableForImputation 
                      6.0000000                   2.0511976 
    
    test_kNN.R....................    3 tests OK 
    test_kNN.R....................    4 tests OK 
    test_kNN.R....................    4 tests OK z z 
    1 6 
    
    test_kNN.R....................    4 tests OK 
    test_kNN.R....................    5 tests OK 
    test_kNN.R....................    5 tests OK z z 
    1 6 
    
    test_kNN.R....................    5 tests OK 
    test_kNN.R....................    6 tests OK 
    test_kNN.R....................    7 tests OK 
    test_kNN.R....................    7 tests OK 
    test_kNN.R....................    8 tests OK 
    test_kNN.R....................    9 tests OK 
    test_kNN.R....................    9 tests OK 
    test_kNN.R....................    9 tests OK z z 
    1 6 
    y y 
    1 6 
    
    test_kNN.R....................    9 tests OK 
    test_kNN.R....................   10 tests OK 
    test_kNN.R....................   11 tests OK z z 
    1 6 
    y y 
    1 5 
    
    test_kNN.R....................   11 tests OK 
    test_kNN.R....................   12 tests OK 
    test_kNN.R....................   13 tests OK 
    test_kNN.R....................   13 tests OK 
    test_kNN.R....................   13 tests OK 
    test_kNN.R....................   13 tests OK          y          z          m          y          z          m 
     1.0000000  1.0000000 -0.2369185  6.0000000  6.0000000  1.0393184 
             z          m          z          m 
     1.0000000 -0.2369185  6.0000000  1.0393184 
    y z y z 
    1 1 6 6 
             y          z          m          y          z          m 
     1.0000000  1.0000000 -0.2369185  6.0000000  6.0000000  1.0393184 
                       y                    z                    m 
               1.0000000            1.0000000           -0.2369185 
    yrandomForestFeature                    y                    z 
               1.9946333            6.0000000            6.0000000 
                       m yrandomForestFeature 
               1.0393184            4.8171000 
                       y                    z                    m 
              1.00000000           1.00000000          -0.23691848 
    mrandomForestFeature                    y                    z 
             -0.03045094           6.00000000           6.00000000 
                       m mrandomForestFeature 
              1.03931837           0.90111834 
    
    test_kNN.R....................   13 tests OK 
    test_kNN.R....................   14 tests OK 
    test_kNN.R....................   15 tests OK 
    test_kNN.R....................   15 tests OK 
    test_kNN.R....................   15 tests OK 
    test_kNN.R....................   15 tests OK          y          z          m          y          z          m 
     1.0000000  1.0000000 -0.2369185  6.0000000  6.0000000  1.0393184 
             z          m          z          m 
     1.0000000 -0.2369185  6.0000000  1.0393184 
    y z y z 
    1 1 6 6 
    yrandomForestFeature yrandomForestFeature 
                2.082400             4.727667 
    mrandomForestFeature mrandomForestFeature 
             -0.05045148           0.87127575 
    
    test_kNN.R....................   15 tests OK 
    test_kNN.R....................   16 tests OK 
    test_kNN.R....................   17 tests OK 
    test_kNN.R....................   17 tests OK 
    test_kNN.R....................   17 tests OK z y z y 
    1 1 6 6 
    z z 
    1 6 
    z z 
    1 6 
                       z yrandomForestFeature                    z 
                1.000000             2.032967             6.000000 
    yrandomForestFeature 
                4.985967 
    
    test_kNN.R....................   17 tests OK 
    test_kNN.R....................   18 tests OK 
    test_kNN.R....................   19 tests OK 
    test_kNN.R....................   19 tests OK 
    test_kNN.R....................   19 tests OK z y z y 
    1 1 6 6 
    z z 
    1 6 
    z z 
    1 6 
                       z yrandomForestFeature                    z 
                1.000000             1.902000             6.000000 
    yrandomForestFeature 
                4.915067 
    
    test_kNN.R....................   19 tests OK 
    test_kNN.R....................   20 tests OK 
    test_kNN.R....................   21 tests OK 
    test_kNN.R....................   21 tests OK 
    test_kNN.R....................   21 tests OK y y 
    1 6 
    
    test_kNN.R....................   21 tests OK 
    test_kNN.R....................   21 tests OK y y 
    1 6 
    
    test_kNN.R....................   21 tests OK 
    test_kNN.R....................   21 tests OK 
    test_kNN.R....................   22 tests OK 
    test_kNN.R....................   22 tests OK 
    test_kNN.R....................   22 tests OK Detected as categorical variable:
    
    x,x_imp,y_imp
    Detected as ordinal variable:
    
    
    Detected as numerical variable:
    
    y
    0 items ofvariable:x imputed
    
    6items ofvariable:y imputed
    
    Time difference of 0.01598048 secs
    
    test_kNN.R....................   22 tests OK 
    test_kNN.R....................   23 tests OK 
    test_kNN.R....................   23 tests OK 
    test_kNN.R....................   23 tests OK 
    test_kNN.R....................   23 tests OK                           y                           z 
                       1.000000                    1.000000 
    RandomVariableForImputation                           y 
                      -1.130797                    6.000000 
                              z RandomVariableForImputation 
                       6.000000                    1.380325 
                              z RandomVariableForImputation 
                       1.000000                   -1.130797 
                              z RandomVariableForImputation 
                       6.000000                    1.380325 
                              y                           z 
                       1.000000                    1.000000 
    RandomVariableForImputation                           y 
                      -1.130797                    6.000000 
                              z RandomVariableForImputation 
                       6.000000                    1.380325 
    
    test_kNN.R....................   23 tests OK 
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   24 tests OK                           y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.6220983                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              z                          y2 
                      1.0000000                   1.0000000 
                             z2                          m2 
                      1.0000000                  -0.2369185 
                            y23                         z23 
                      1.0000000                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.6220983 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.6220983                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.6220983                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             z2                          m2 
                      1.0000000                  -0.2369185 
                            y23                         z23 
                      1.0000000                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.6220983 
                              y                           z 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                            y23                         z23 
                      1.0000000                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.6220983 
                              y                           z 
                      6.0000000                   6.0000000 
                             y2                          z2 
                      6.0000000                   6.0000000 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23                         m23 
                      1.0000000                  -0.2369185 
    RandomVariableForImputation                           y 
                     -1.6220983                   6.0000000 
                              z                          y2 
                      6.0000000                   6.0000000 
                             z2                          m2 
                      6.0000000                   1.0393184 
                            y23                         z23 
                      6.0000000                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         z23 
                     -0.2369185                   1.0000000 
                            m23 RandomVariableForImputation 
                     -0.2369185                  -1.6220983 
                              y                           z 
                      6.0000000                   6.0000000 
                             y2                          z2 
                      6.0000000                   6.0000000 
                             m2                         z23 
                      1.0393184                   6.0000000 
                            m23 RandomVariableForImputation 
                      1.0393184                   1.1502594 
                              y                           z 
                      1.0000000                   1.0000000 
                             y2                          z2 
                      1.0000000                   1.0000000 
                             m2                         y23 
                     -0.2369185                   1.0000000 
                            z23 RandomVariableForImputation 
                      1.0000000                  -1.6220983 
                              y                           z 
                      6.0000000                   6.0000000 
                             y2                          z2 
                      6.0000000                   6.0000000 
                             m2                         y23 
                      1.0393184                   6.0000000 
                            z23 RandomVariableForImputation 
                      6.0000000                   1.1502594 
    
    test_kNN.R....................   24 tests OK 
    test_kNN.R....................   25 tests OK 
    test_kNN.R....................   25 tests OK z z 
    1 6 
    
    test_kNN.R....................   25 tests OK 
    test_kNN.R....................   26 tests OK 
    test_kNN.R....................   26 tests OK 
    test_kNN.R....................   27 tests OK 
    test_kNN.R....................   28 tests OK 
    test_kNN.R....................   28 tests OK 
    test_kNN.R....................   28 tests OK 
    test_kNN.R....................   28 tests OK          y          z          m          y          z          m 
     1.0000000  1.0000000 -0.2369185  6.0000000  6.0000000  1.0393184 
             z          m          z          m 
     1.0000000 -0.2369185  6.0000000  1.0393184 
    y z y z 
    1 1 6 6 
             y          z          m          y          z          m 
     1.0000000  1.0000000 -0.2369185  6.0000000  6.0000000  1.0393184 
                       y                    z                    m 
               1.0000000            1.0000000           -0.2369185 
    yrandomForestFeature                    y                    z 
               2.1257000            6.0000000            6.0000000 
                       m yrandomForestFeature 
               1.0393184            4.6920333 
                       y                    z                    m 
              1.00000000           1.00000000          -0.23691848 
    mrandomForestFeature                    y                    z 
             -0.04213089           6.00000000           6.00000000 
                       m mrandomForestFeature 
              1.03931837           0.84783840 
    
    test_kNN.R....................   28 tests OK 
    test_kNN.R....................   29 tests OK 
    test_kNN.R....................   30 tests OK 
    test_kNN.R....................   30 tests OK 
    test_kNN.R....................   30 tests OK 
    test_kNN.R....................   30 tests OK          y          z          m          y          z          m 
     1.0000000  1.0000000 -0.2369185  6.0000000  6.0000000  1.0393184 
             z          m          z          m 
     1.0000000 -0.2369185  6.0000000  1.0393184 
    y z y z 
    1 1 6 6 
    yrandomForestFeature yrandomForestFeature 
                2.125167             4.833167 
    mrandomForestFeature mrandomForestFeature 
             -0.09882801           0.85004137 
    
    test_kNN.R....................   30 tests OK 
    test_kNN.R....................   31 tests OK 
    test_kNN.R....................   32 tests OK 
    test_kNN.R....................   32 tests OK 
    test_kNN.R....................   32 tests OK z y z y 
    1 1 6 6 
    z z 
    1 6 
    z z 
    1 6 
                       z yrandomForestFeature                    z 
                1.000000             1.962533             6.000000 
    yrandomForestFeature 
                4.974033 
    
    test_kNN.R....................   32 tests OK 
    test_kNN.R....................   33 tests OK 
    test_kNN.R....................   34 tests OK 
    test_kNN.R....................   34 tests OK 
    test_kNN.R....................   34 tests OK z y z y 
    1 1 6 6 
    z z 
    1 6 
    z z 
    1 6 
                       z yrandomForestFeature                    z 
                1.000000             1.989633             6.000000 
    yrandomForestFeature 
                4.963533 
    
    test_kNN.R....................   34 tests OK 
    test_kNN.R....................   35 tests OK 
    test_kNN.R....................   36 tests OK 
    test_kNN.R....................   36 tests OK col2 col3 col2 col3 
       3    5    4    6 
    
    test_kNN.R....................   37 tests OK 
    test_kNN.R....................   37 tests OK 
    test_kNN.R....................   37 tests OK col2 col3 col2 col3 
       3    5    4    6 
    
    test_kNN.R....................   38 tests OK 
    test_kNN.R....................   38 tests OK 
    test_kNN.R....................   38 tests OK col2 col3 col2 col3 
       3    5    4    6 
    
    test_kNN.R....................   39 tests OK weighted catFun without missings in the distance variables
    
    test_kNN.R....................   39 tests OK 
    test_kNN.R....................   39 tests OK 
    test_kNN.R....................   39 tests OK  x  y  x  y 
     1  1 10 10 
    
    test_kNN.R....................   39 tests OK weighted catFun with missings in the distance variables
    
    test_kNN.R....................   39 tests OK 
    test_kNN.R....................   39 tests OK  x  y  x  y 
     1  1 10 10 
    
    test_kNN.R....................   39 tests OK  x  y  x  y 
     1  1 10 10 
    
    test_kNN.R....................   39 tests OK 1.7s
    
    test_kNN_exact.R..............    0 tests    kNN exact results
    
    test_kNN_exact.R..............    0 tests    
    test_kNN_exact.R..............    0 tests    
    test_kNN_exact.R..............    0 tests    
    test_kNN_exact.R..............    0 tests    Detected as categorical variable:
    
    Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp
    Detected as ordinal variable:
    
    ord
    Detected as numerical variable:
    
    X1,X2,ClassNum,Row,Row2
    0 items ofvariable:Class imputed
    
    0 items ofvariable:X1 imputed
    
    X1 X1 
     1  1 
    2items ofvariable:X2 imputed
    
    0 items ofvariable:ClassNum imputed
    
    0 items ofvariable:Row imputed
    
    0 items ofvariable:Row2 imputed
    
    0 items ofvariable:ord imputed
    
    Time difference of 0.02307701 secs
    
    test_kNN_exact.R..............    0 tests    
    test_kNN_exact.R..............    1 tests OK 
    test_kNN_exact.R..............    2 tests OK 
    test_kNN_exact.R..............    2 tests OK 
    test_kNN_exact.R..............    3 tests OK 
    test_kNN_exact.R..............    4 tests OK       X1 ClassNum       X1 ClassNum 
           1        1        1        2 
    
    test_kNN_exact.R..............    4 tests OK 
    test_kNN_exact.R..............    5 tests OK 
    test_kNN_exact.R..............    6 tests OK 
    test_kNN_exact.R..............    6 tests OK 
    test_kNN_exact.R..............    6 tests OK Row2 Row2 
       1   10 
    Row2 Row2 
       1   10 
    
    test_kNN_exact.R..............    6 tests OK 
    test_kNN_exact.R..............    7 tests OK 
    test_kNN_exact.R..............    8 tests OK Row2 Row2 
       1   10 
    Row2 Row2 
       1   10 
    
    test_kNN_exact.R..............    8 tests OK 
    test_kNN_exact.R..............    9 tests OK 
    test_kNN_exact.R..............   10 tests OK   X1 Row2   X1 Row2 
       1    1    1   10 
      X1 Row2   X1 Row2 
       1    1    1   10 
    
    test_kNN_exact.R..............   10 tests OK 
    test_kNN_exact.R..............   11 tests OK 
    test_kNN_exact.R..............   12 tests OK 
    test_kNN_exact.R..............   12 tests OK 
    test_kNN_exact.R..............   12 tests OK 
    test_kNN_exact.R..............   12 tests OK 
    test_kNN_exact.R..............   13 tests OK 
    test_kNN_exact.R..............   14 tests OK Row2 Row2 
       1   10 
    Row2 Row2 
       1   10 
    
    test_kNN_exact.R..............   14 tests OK 
    test_kNN_exact.R..............   15 tests OK 
    test_kNN_exact.R..............   16 tests OK   X1 Row2   X1 Row2 
       1    1    1   10 
      X1 Row2   X1 Row2 
       1    1    1   10 
    
    test_kNN_exact.R..............   16 tests OK 
    test_kNN_exact.R..............   17 tests OK 
    test_kNN_exact.R..............   18 tests OK 
    test_kNN_exact.R..............   18 tests OK 
    test_kNN_exact.R..............   18 tests OK 
    test_kNN_exact.R..............   18 tests OK Detected as categorical variable:
    
    Class,Class_imp,X1_imp,X2_imp,ClassNum_imp,Row_imp,Row2_imp,ord_imp
    Detected as ordinal variable:
    
    ord
    Detected as numerical variable:
    
    X1,X2,ClassNum,Row,Row2
    0 items ofvariable:Class imputed
    
    0 items ofvariable:X1 imputed
    
    X1 X1 
     1  1 
    2items ofvariable:X2 imputed
    
    0 items ofvariable:ClassNum imputed
    
    0 items ofvariable:Row imputed
    
    0 items ofvariable:Row2 imputed
    
    0 items ofvariable:ord imputed
    
    Time difference of 0.01501894 secs
    
    test_kNN_exact.R..............   18 tests OK 
    test_kNN_exact.R..............   19 tests OK 
    test_kNN_exact.R..............   20 tests OK 
    test_kNN_exact.R..............   20 tests OK 
    test_kNN_exact.R..............   21 tests OK 
    test_kNN_exact.R..............   22 tests OK       X1 ClassNum       X1 ClassNum 
           1        1        1        2 
    
    test_kNN_exact.R..............   22 tests OK 
    test_kNN_exact.R..............   23 tests OK 
    test_kNN_exact.R..............   24 tests OK 
    test_kNN_exact.R..............   24 tests OK 
    test_kNN_exact.R..............   24 tests OK Row2 Row2 
       1   10 
    Row2 Row2 
       1   10 
    
    test_kNN_exact.R..............   24 tests OK 
    test_kNN_exact.R..............   25 tests OK 
    test_kNN_exact.R..............   26 tests OK Row2 Row2 
       1   10 
    Row2 Row2 
       1   10 
    
    test_kNN_exact.R..............   26 tests OK 
    test_kNN_exact.R..............   27 tests OK 
    test_kNN_exact.R..............   28 tests OK   X1 Row2   X1 Row2 
       1    1    1   10 
      X1 Row2   X1 Row2 
       1    1    1   10 
    
    test_kNN_exact.R..............   28 tests OK 
    test_kNN_exact.R..............   29 tests OK 
    test_kNN_exact.R..............   30 tests OK 
    test_kNN_exact.R..............   30 tests OK 
    test_kNN_exact.R..............   30 tests OK 
    test_kNN_exact.R..............   30 tests OK 
    test_kNN_exact.R..............   31 tests OK 
    test_kNN_exact.R..............   32 tests OK Row2 Row2 
       1   10 
    Row2 Row2 
       1   10 
    
    test_kNN_exact.R..............   32 tests OK 
    test_kNN_exact.R..............   33 tests OK 
    test_kNN_exact.R..............   34 tests OK   X1 Row2   X1 Row2 
       1    1    1   10 
      X1 Row2   X1 Row2 
       1    1    1   10 
    
    test_kNN_exact.R..............   34 tests OK 
    test_kNN_exact.R..............   35 tests OK 
    test_kNN_exact.R..............   36 tests OK 0.3s
    
    test_kNN_iqr.R................    0 tests    kNN iqr
    
    test_kNN_iqr.R................    0 tests    
    test_kNN_iqr.R................    0 tests    
    test_kNN_iqr.R................    0 tests    
    test_kNN_iqr.R................    0 tests            y         z         y         z 
    -1.914359 -2.888921  2.307978  2.649167 
    
    test_kNN_iqr.R................    0 tests    
    test_kNN_iqr.R................    0 tests    
    test_kNN_iqr.R................    1 tests OK 23ms
    
    test_kNN_ordered.R............    0 tests    kNN ordered
    
    test_kNN_ordered.R............    0 tests    
    test_kNN_ordered.R............    0 tests    
    test_kNN_ordered.R............    0 tests    
    test_kNN_ordered.R............    0 tests    y z y z 
    1 1 6 6 
    
    test_kNN_ordered.R............    0 tests    
    test_kNN_ordered.R............    1 tests OK 
    test_kNN_ordered.R............    2 tests OK y z y z 
    1 1 6 6 
    
    test_kNN_ordered.R............    2 tests OK 
    test_kNN_ordered.R............    3 tests OK 
    test_kNN_ordered.R............    4 tests OK y z y z 
    1 1 6 6 
    
    test_kNN_ordered.R............    4 tests OK 
    test_kNN_ordered.R............    5 tests OK 
    test_kNN_ordered.R............    6 tests OK y z y z 
    1 1 6 6 
    
    test_kNN_ordered.R............    6 tests OK 
    test_kNN_ordered.R............    7 tests OK 
    test_kNN_ordered.R............    8 tests OK 57ms
    
    test_matchImpute.R............    0 tests    matchImpute general
    
    test_matchImpute.R............    0 tests    
    test_matchImpute.R............    0 tests    
    test_matchImpute.R............    0 tests    
    test_matchImpute.R............    0 tests    
    test_matchImpute.R............    0 tests    
    test_matchImpute.R............    1 tests OK 
    test_matchImpute.R............    1 tests OK 
    test_matchImpute.R............    2 tests OK 
    test_matchImpute.R............    3 tests OK 
    test_matchImpute.R............    4 tests OK 
    test_matchImpute.R............    5 tests OK 
    test_matchImpute.R............    5 tests OK 
    test_matchImpute.R............    6 tests OK 14ms
    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    0 tests    
    test_rangerImpute.R...........    1 tests OK No missings in x.
    
    test_rangerImpute.R...........    1 tests OK 
    test_rangerImpute.R...........    2 tests OK 
    test_rangerImpute.R...........    2 tests OK 
    test_rangerImpute.R...........    2 tests OK 
    test_rangerImpute.R...........    3 tests OK 
    test_rangerImpute.R...........    3 tests OK 
    test_rangerImpute.R...........    4 tests OK 
    test_rangerImpute.R...........    4 tests OK 
    test_rangerImpute.R...........    4 tests OK 
    test_rangerImpute.R...........    4 tests OK 
    test_rangerImpute.R...........    5 tests OK 0.1s
    
    test_regressionImp.R..........    0 tests    
    test_regressionImp.R..........    0 tests    
    test_regressionImp.R..........    1 tests OK 
    test_regressionImp.R..........    1 tests OK 
    test_regressionImp.R..........    2 tests OK 
    test_regressionImp.R..........    2 tests OK 
    test_regressionImp.R..........    3 tests OK 
    test_regressionImp.R..........    3 tests OK 
    test_regressionImp.R..........    4 tests OK 
    test_regressionImp.R..........    4 tests OK 
    test_regressionImp.R..........    4 tests OK 
    test_regressionImp.R..........    4 tests OK 
    test_regressionImp.R..........    4 tests OK 
    test_regressionImp.R..........    5 tests OK 10ms
    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    0 tests    
    test_xgboostImpute.R..........    1 tests OK No missings in x.
    
    test_xgboostImpute.R..........    1 tests OK 
    test_xgboostImpute.R..........    2 tests OK Error in process.y.margin.and.objective(y, base_margin, objective, params) : 
      Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Calls: <Anonymous> ... xgboostImpute -> <Anonymous> -> process.y.margin.and.objective
    In addition: There were 20 warnings (use warnings() to see them)
    Execution halted

Package: visaOTR
Check: examples
New result: ERROR
  Running examples in ‘visaOTR-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: visa.est
  > ### Title: Valid Improved Sparsity A-Learning for Optimal Treatment
  > ###   Decision
  > ### Aliases: visa.est
  > 
  > ### ** Examples
  > 
  > data(visa_SimuData)
  > y = visa_SimuData$y
  > a = visa_SimuData$a
  > x = visa_SimuData$x
  > # estimation
  > result <- visa.est(y, x, a, IC = "BIC", lambda.list = c(0.1, 0.5))
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in parallel::mclapply(seq(no_rep), wt_calc) :
    all scheduled cores encountered errors in user code
  Error in FUN(x, aperm(array(STATS, dims[perm]), order(perm)), ...) : 
    non-numeric argument to binary operator
  Calls: visa.est -> visa.weight -> sweep
  Execution halted

Package: weightedGCM
Check: R code for possible problems
New result: NOTE
  train.xgboost1: no visible global function definition for
    ‘cb.evaluation.log’
  Undefined global functions or variables:
    cb.evaluation.log

Package: xgb2sql
Check: examples
New result: ERROR
  Running examples in ‘xgb2sql-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: booster2sql
  > ### Title: Transform XGBoost model object to SQL query.
  > ### Aliases: booster2sql
  > 
  > ### ** Examples
  > 
  > library(xgboost)
  > # load data
  > df = data.frame(ggplot2::diamonds)
  > head(df)
    carat       cut color clarity depth table price    x    y    z
  1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
  2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
  3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
  4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
  5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
  6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48
  > 
  > # data processing
  > out <- onehot2sql(df)
  > x <- out$model.matrix[,colnames(out$model.matrix)!='price']
  > y <- out$model.matrix[,colnames(out$model.matrix)=='price']
  > 
  > # model training
  > bst <- xgboost(data = x,
  +                label = y,
  +                max.depth = 3,
  +                eta = .3,
  +                nround = 5,
  +                nthread = 1,
  +                objective = 'reg:linear')
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: max.depth. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'eta' has been renamed to 'learning_rate'. This warning will become an error in a future version.
  Error in process.y.margin.and.objective(y, base_margin, objective, params) : 
    Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: reg:linear
  Calls: xgboost -> process.y.margin.and.objective
  Execution halted

Package: xgb2sql
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘xgb2sql.Rmd’ using rmarkdown
  
  Quitting from xgb2sql.Rmd:221-231 [unnamed-chunk-6]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `process.y.margin.and.objective()`:
  ! Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: reg:linear
  ---
  Backtrace:
      ▆
   1. └─xgboost::xgboost(...)
   2.   └─xgboost:::process.y.margin.and.objective(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'xgb2sql.Rmd' failed with diagnostics:
  Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: reg:linear
  --- failed re-building ‘xgb2sql.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘xgb2sql.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: xrf
Check: examples
New result: ERROR
  Running examples in ‘xrf-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: summary.xrf
  > ### Title: Summarize an eXtreme RuleFit model
  > ### Aliases: summary.xrf
  > 
  > ### ** Examples
  > 
  > m <- xrf(Petal.Length ~ ., iris,
  +          xgb_control = list(nrounds = 2, max_depth = 2),
  +          family = 'gaussian')
  Warning in throw_err_or_depr_msg("Parameter(s) have been removed from this function: ",  :
    Parameter(s) have been removed from this function: params. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Passed unrecognized parameters: ", paste(head(names_unrecognized),  :
    Passed unrecognized parameters: verbose. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'data' has been renamed to 'x'. This warning will become an error in a future version.
  Warning in throw_err_or_depr_msg("Parameter '", match_old, "' has been renamed to '",  :
    Parameter 'label' has been renamed to 'y'. This warning will become an error in a future version.
  > summary(m)
  An eXtreme RuleFit model of 11 rules.
  
  Original Formula:
  
  Petal.Length ~ Sepal.Length + Sepal.Width + Petal.Width + Species
  
  Tree model:
  
  Error in h(simpleError(msg, call)) : 
    error in evaluating the argument 'object' in selecting a method for function 'show': length of 'dimnames' [1] not equal to array extent
  Calls: summary ... summary.default -> array -> .handleSimpleError -> h
  Execution halted

Package: xrf
Check: tests
New result: ERROR
    Running ‘testthat.R’ [11s/11s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(xrf)
    > 
    > test_check("xrf")
    Saving _problems/test_model-39.R
    Saving _problems/test_model-48.R
    Saving _problems/test_model-56.R
    [ FAIL 3 | WARN 44 | SKIP 0 | PASS 12 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test_model.R:38:3'): model from dense design matrix has expected fields ──
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. ├─xrf::xrf(...) at test_model.R:38:3
     2. └─xrf:::xrf.formula(...)
     3.   └─xgboost::xgboost(...)
     4.     └─xgboost:::process.y.margin.and.objective(...)
    ── Error ('test_model.R:47:3'): model from sparse design matrix has expected fields ──
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. ├─xrf::xrf(...) at test_model.R:47:3
     2. └─xrf:::xrf.formula(...)
     3.   └─xgboost::xgboost(...)
     4.     └─xgboost:::process.y.margin.and.objective(...)
    ── Error ('test_model.R:54:3'): model predicts binary outcome ──────────────────
    Error in `process.y.margin.and.objective(y, base_margin, objective, params)`: Got numeric 'y' - supported objectives for this data are: reg:squarederror, reg:squaredlogerror, reg:logistic, reg:pseudohubererror, reg:absoluteerror, reg:quantileerror, count:poisson, reg:gamma, reg:tweedie. Was passed: binary:logistic
    Backtrace:
        ▆
     1. ├─xrf::xrf(...) at test_model.R:54:3
     2. └─xrf:::xrf.formula(...)
     3.   └─xgboost::xgboost(...)
     4.     └─xgboost:::process.y.margin.and.objective(...)
    
    [ FAIL 3 | WARN 44 | SKIP 0 | PASS 12 ]
    Error:
    ! Test failures.
    Execution halted