Package check result: OK

Changes to worse in reverse depends:

Package: APCtools
Check: examples
New result: ERROR
  Running examples in ‘APCtools-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: plot_1Dsmooth
  > ### Title: Plot 1D smooth effects for 'gam' models
  > ### Aliases: plot_1Dsmooth
  > 
  > ### ** Examples
  > 
  > library(APCtools)
  > library(mgcv)
  Loading required package: nlme
  This is mgcv 1.9-4. For overview type '?mgcv'.
  > 
  > data(travel)
  > model <- gam(mainTrip_distance ~ te(age, period) + residence_region +
  +              household_size + s(household_income), data = travel)
  > 
  > plot_1Dsmooth(model, select = 2)
  Error in data.frame(x = plotObject$x, y = plotObject$fit, se = plotObject$se/plotObject$se.mult) : 
    arguments imply differing number of rows: 100, 0
  Calls: plot_1Dsmooth -> %>% -> mutate -> data.frame
  Execution halted

Package: APCtools
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘main_functionality.Rmd’ using rmarkdown
  
  Quitting from main_functionality.Rmd:385-388 [unnamed-chunk-18]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `data.frame()`:
  ! arguments imply differing number of rows: 100, 0
  ---
  Backtrace:
      ▆
   1. ├─APCtools::plot_1Dsmooth(model_cov, select = 2)
   2. │ ├─... %>% ...
   3. │ └─base::data.frame(x = plotObject$x, y = plotObject$fit, se = plotObject$se/plotObject$se.mult)
   4. └─dplyr::mutate(...)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'main_functionality.Rmd' failed with diagnostics:
  arguments imply differing number of rows: 100, 0
  --- failed re-building ‘main_functionality.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘main_functionality.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: APCtools
Check: tests
New result: ERROR
    Running ‘testthat.R’ [50s/50s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(APCtools)
    > 
    > test_check("APCtools")
    Loading required package: nlme
    This is mgcv 1.9-4. For overview type '?mgcv'.
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 10 missing observations of household_size...
    Excluding 10 missing observations of household_size...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 10 missing observations of household_size...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 9565 missing observations of mainTrip_distance...
    Excluding 2743 missing observations of mainTrip_distance...
    Excluding 2743 missing observations of mainTrip_distance...
    Excluding 2743 missing observations of mainTrip_distance...
    Excluding 2950 missing observations of mainTrip_distance...
    Excluding 2923 missing observations of mainTrip_distance...
    [ FAIL 1 | WARN 67 | SKIP 0 | PASS 90 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-plots_covarEffects.R:52:3'): plot_1Dsmooth ─────────────────────
    Error in `data.frame(x = plotObject$x, y = plotObject$fit, se = plotObject$se/plotObject$se.mult)`: arguments imply differing number of rows: 100, 0
    Backtrace:
        ▆
     1. ├─APCtools::plot_1Dsmooth(model, select = 2, alpha = 0.1) at test-plots_covarEffects.R:52:3
     2. │ ├─... %>% ...
     3. │ └─base::data.frame(x = plotObject$x, y = plotObject$fit, se = plotObject$se/plotObject$se.mult)
     4. └─dplyr::mutate(...)
    
    [ FAIL 1 | WARN 67 | SKIP 0 | PASS 90 ]
    Error: Test failures
    Execution halted

Package: elastes
Check: package dependencies
New result: ERROR
  Package required but not available: ‘sparseFLMM’
  
  See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
  manual.

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

Package: geoGAM
Check: examples
New result: ERROR
  Running examples in ‘geoGAM-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: geoGAM
  > ### Title: Select sparse geoadditive model
  > ### Aliases: geoGAM
  > ### Keywords: spatial models & regression & nonlinear
  > 
  > ### ** Examples
  > 
  > ### small examples with earthquake data
  > 
  > data(quakes)
  > set.seed(2)
  > quakes <- quakes[ sample(1:nrow(quakes), 50), ]
  > 
  > quakes.geogam <- geoGAM(response = "mag",
  +                         covariates = c("depth", "stations"),
  +                         data = quakes,
  +                         seed = 2,
  +                         max.stop = 5,
  +                         cores = 1)
  Warning in regularize.values(x, y, ties, missing(ties)) :
    collapsing to unique 'x' values
  Warning in regularize.values(x, y, ties, missing(ties)) :
    collapsing to unique 'x' values
  Error in if (sum(fix.ind)) { : argument is not interpretable as logical
  Calls: geoGAM ... mclapply -> lapply -> FUN -> gam -> do.call -> gam.setup
  Execution halted

Package: gmfamm
Check: examples
New result: ERROR
  Running examples in ‘gmfamm-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: mface_cyc
  > ### Title: Multilevel functional principal components analysis with fast
  > ###   covariance estimation
  > ### Aliases: mface_cyc
  > 
  > ### ** Examples
  > 
  > require(refund)
  Loading required package: refund
  Error: package or namespace load failed for ‘refund’:
   object ‘gam.fit’ is not exported by 'namespace:mgcv'
  > data(DTI)
  Warning in data(DTI) : data set ‘DTI’ not found
  > mfpca.DTI <- mfpca.face(Y = DTI$cca, id = DTI$ID, twoway = TRUE)
  Error in mfpca.face(Y = DTI$cca, id = DTI$ID, twoway = TRUE) : 
    could not find function "mfpca.face"
  Execution halted

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

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

Package: MJMbamlss
Check: package dependencies
New result: ERROR
  Package required but not available: ‘sparseFLMM’
  
  See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
  manual.

Package: multifamm
Check: package dependencies
New result: ERROR
  Package required but not available: ‘sparseFLMM’
  
  See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
  manual.

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

Package: ordPens
Check: examples
New result: ERROR
  Running examples in ‘ordPens-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: ordSmooth
  > ### Title: Smoothing dummy coefficients of ordinal predictors
  > ### Aliases: ordSmooth
  > ### Keywords: models regression
  > 
  > ### ** Examples
  > 
  > # smooth modeling of a simulated dataset
  > set.seed(123)
  > 
  > # generate (ordinal) predictors
  > x1 <- sample(1:8,100,replace=TRUE)
  > x2 <- sample(1:6,100,replace=TRUE)
  > x3 <- sample(1:7,100,replace=TRUE)
  > 
  > # the response
  > y <- -1 + log(x1) + sin(3*(x2-1)/pi) + rnorm(100)
  > 
  > # x matrix
  > x <- cbind(x1,x2,x3)
  > 
  > # lambda values
  > lambda <- c(1000,500,200,100,50,30,20,10,1)
  > 
  > # smooth modeling
  > osm1 <- ordSmooth(x = x, y = y, lambda = lambda)
  > 
  > # results
  > round(osm1$coef,digits=3)
              1000    500    200    100     50     30     20     10      1
  intercept  0.190  0.163  0.088 -0.019 -0.186 -0.349 -0.497 -0.768 -1.405
  x1:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  x1:2       0.021  0.041  0.093  0.161  0.260  0.353  0.439  0.605  1.137
  x1:3       0.038  0.073  0.162  0.273  0.419  0.542  0.640  0.797  1.057
  x1:4       0.057  0.109  0.239  0.399  0.604  0.768  0.897  1.099  1.481
  x1:5       0.077  0.146  0.319  0.530  0.797  1.010  1.177  1.444  2.080
  x1:6       0.093  0.177  0.383  0.630  0.934  1.163  1.332  1.577  1.977
  x1:7       0.108  0.204  0.442  0.723  1.062  1.309  1.484  1.720  2.057
  x1:8       0.124  0.236  0.511  0.837  1.232  1.525  1.738  2.042  2.598
  x2:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  x2:2       0.000  0.001  0.011  0.039  0.104  0.186  0.269  0.431  0.781
  x2:3      -0.008 -0.012 -0.013  0.008  0.080  0.182  0.292  0.521  1.153
  x2:4      -0.028 -0.051 -0.099 -0.136 -0.145 -0.118 -0.076  0.018  0.164
  x2:5      -0.052 -0.097 -0.200 -0.305 -0.402 -0.445 -0.456 -0.438 -0.342
  x2:6      -0.079 -0.149 -0.314 -0.497 -0.696 -0.822 -0.897 -0.969 -0.964
  x3:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  x3:2      -0.001 -0.003 -0.005 -0.006 -0.002  0.010  0.027  0.074  0.243
  x3:3       0.001  0.002  0.003  0.006  0.013  0.022  0.033  0.057  0.091
  x3:4       0.001  0.001  0.001  0.001  0.000  0.000  0.001  0.005 -0.020
  x3:5       0.001  0.001  0.001  0.000 -0.003 -0.004 -0.003  0.005  0.066
  x3:6      -0.001 -0.001 -0.003 -0.005 -0.004  0.003  0.014  0.049  0.250
  x3:7       0.002  0.004  0.010  0.020  0.042  0.069  0.097  0.153  0.278
  > plot(osm1)
  > 
  > # If for a certain plot the x-axis should be annotated in a different way,
  > # this can (for example) be done as follows:
  > plot(osm1, whx = 1, xlim = c(0,9), xaxt = "n")
  > axis(side = 1, at = c(1,8), labels = c("no agreement","total agreement"))
  > 
  > # add a nominal covariate to control for
  > u1 <- sample(1:8,100,replace=TRUE)
  > u <- cbind(u1)
  > osm2 <- ordSmooth(x = x, y = y, u = u, lambda = lambda)
  > round(osm2$coef,digits=3)
              1000    500    200    100     50     30     20     10      1
  intercept -0.028 -0.046 -0.099 -0.181 -0.325 -0.477 -0.624 -0.905 -1.601
  x1:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  x1:2       0.020  0.039  0.088  0.155  0.254  0.348  0.436  0.609  1.181
  x1:3       0.035  0.067  0.150  0.256  0.400  0.523  0.624  0.788  1.078
  x1:4       0.053  0.100  0.222  0.374  0.575  0.740  0.873  1.086  1.530
  x1:5       0.070  0.134  0.294  0.493  0.751  0.961  1.128  1.398  2.040
  x1:6       0.085  0.161  0.353  0.586  0.878  1.104  1.273  1.524  1.959
  x1:7       0.098  0.186  0.406  0.670  0.997  1.242  1.420  1.669  2.070
  x1:8       0.109  0.208  0.453  0.749  1.115  1.391  1.595  1.894  2.473
  x2:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  x2:2      -0.001 -0.001  0.005  0.027  0.086  0.163  0.245  0.414  0.844
  x2:3      -0.009 -0.016 -0.022 -0.009  0.053  0.147  0.255  0.489  1.205
  x2:4      -0.030 -0.055 -0.109 -0.155 -0.176 -0.156 -0.116 -0.019  0.173
  x2:5      -0.054 -0.100 -0.208 -0.322 -0.428 -0.476 -0.488 -0.462 -0.313
  x2:6      -0.080 -0.150 -0.318 -0.507 -0.714 -0.843 -0.918 -0.982 -0.908
  x3:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  x3:2      -0.001 -0.001 -0.002 -0.001  0.006  0.019  0.038  0.090  0.308
  x3:3       0.002  0.004  0.008  0.014  0.024  0.037  0.050  0.077  0.100
  x3:4       0.002  0.003  0.006  0.008  0.009  0.009  0.009  0.006 -0.075
  x3:5       0.002  0.004  0.008  0.010  0.010  0.010  0.012  0.020  0.097
  x3:6       0.001  0.002  0.003  0.004  0.007  0.016  0.029  0.072  0.318
  x3:7       0.003  0.005  0.012  0.021  0.039  0.062  0.088  0.146  0.314
  u1:1       0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000
  u1:2       0.260  0.251  0.231  0.207  0.180  0.160  0.145  0.114 -0.051
  u1:3       0.981  0.962  0.913  0.853  0.775  0.714  0.666  0.590  0.404
  u1:4       0.396  0.388  0.368  0.345  0.319  0.303  0.293  0.282  0.238
  u1:5       0.151  0.145  0.132  0.121  0.116  0.123  0.134  0.163  0.202
  u1:6      -0.337 -0.326 -0.297 -0.264 -0.224 -0.195 -0.177 -0.160 -0.234
  u1:7       0.213  0.202  0.178  0.154  0.137  0.137  0.146  0.174  0.254
  u1:8       0.270  0.273  0.281  0.291  0.303  0.312  0.318  0.321  0.252
  > 
  > 
  > ## Use gam() from mgcv for model fitting:
  > # ordinal predictors need to be ordered factors
  > x1 <- as.ordered(x1)
  > x2 <- as.ordered(x2)
  > x3 <- as.ordered(x3)
  > 
  > # model fitting with first-order penalty and smoothing parameter selection by REML
  > gom1 <- gam(y ~ s(x1, bs = "ordinal", m = 1) + s(x2, bs = "ordinal", m = 1) + 
  + s(x3, bs = "ordinal", m = 1) + factor(u1), method = "REML")
  > 
  > # plot with confidence intervals
  > plot(gom1)
  Error in `[<-`(`*tmp*`, , j, value = P$fit - se.fit * se.mult[j]) : 
    subscript out of bounds
  Calls: plot -> plot.gam
  Execution halted

Package: ordPens
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘ordPCA.Rmd’ using rmarkdown
  [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead.
  --- finished re-building ‘ordPCA.Rmd’
  
  --- re-building ‘ordPens.Rmd’ using rmarkdown
  
  Quitting from ordPens.Rmd:193-198 [test2, figures-side]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `[<-`:
  ! subscript out of bounds
  ---
  Backtrace:
      ▆
   1. ├─base::plot(gom1)
   2. └─mgcv::plot.gam(gom1)
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'ordPens.Rmd' failed with diagnostics:
  subscript out of bounds
  --- failed re-building ‘ordPens.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘ordPens.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: pammtools
Check: examples
New result: ERROR
  Running examples in ‘pammtools-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: gg_tensor
  > ### Title: Plot tensor product effects
  > ### Aliases: gg_tensor
  > 
  > ### ** Examples
  > 
  > g <- mgcv::gam(Sepal.Length ~ te(Sepal.Width, Petal.Length), data=iris)
  > gg_tensor(g)
  Error in matrix(P$ul, n2, n2) : 
    'data' must be of a vector type, was 'NULL'
  Calls: gg_tensor ... plot -> plot.mgcv.smooth -> sp.contour -> median -> matrix
  Execution halted

Package: projpred
Check: dependencies in R code
New result: WARNING
  Missing or unexported object: ‘mgcv::gam.fit’

Package: randnet
Check: package dependencies
New result: ERROR
  Package required but not available: ‘sparseFLMM’
  
  See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
  manual.

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

Package: simml
Check: examples
New result: ERROR
  Running examples in ‘simml-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: simml
  > ### Title: Single-index models with multiple-links (main function)
  > ### Aliases: simml
  > 
  > ### ** Examples
  > 
  > 
  > 
  > family <- "gaussian"   #"poisson"
  > delta = 1              # moderate main effect
  > s=2                    # if s=2 (s=1), a nonlinear (linear) contrast function
  > n=500                  # number of subjects
  > p=10                   # number of pretreatment covariates
  > 
  > # generate training data
  > data <- generate.data(n= n, p=p, delta = delta, s= s, family = family)
  > data$SNR  # the ratio of interactions("signal") vs. main effects("noise")
  [1] 1.034872
  > A <- data$A
  > y <- data$y
  > X <- data$X
  > 
  > # generate testing data
  > data.test <- generate.data(n=10^5, p=p, delta = delta,  s= s, family = family)
  > A.test <- data.test$A
  > y.test <- data.test$y
  > X.test <- data.test$X
  > data.test$value.opt     # the optimal "value"
  [1] 0.3153715
  > 
  > 
  > # fit SIMML
  > #1) SIMML without X main effect
  > simml.obj1 <- simml(y, A, X, family = family)
  iter: 2   difference in beta:  0.04372073 
  iter: 3   difference in beta:  0.001728601 
  Error in fit.simml(y = y, A = A, X = X, Xm = Xm, aug = aug, rho = rho,  : 
    object 'gam.fit' not found
  Calls: simml -> fit.simml
  Execution halted

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

Package: sspm
Check: tests
New result: ERROR
    Running ‘testthat.R’ [99s/100s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(sspm)
    Loading required package: sf
    Linking to GEOS 3.14.1, GDAL 3.11.4, PROJ 9.6.2; sf_use_s2() is TRUE
    WARNING: different compile-time and runtime versions for GEOS found:
    Linked against: 3.14.1-CAPI-1.20.5 compiled against: 3.13.1-CAPI-1.19.2
    It is probably a good idea to reinstall sf (and maybe lwgeom too)
    Loading required package: mgcv
    Loading required package: nlme
    This is mgcv 1.9-4. For overview type '?mgcv'.
    > 
    > test_check("sspm")
    
    Attaching package: 'dplyr'
    
    The following object is masked from 'package:nlme':
    
        collapse
    
    The following object is masked from 'package:testthat':
    
        matches
    
    The following objects are masked from 'package:stats':
    
        filter, lag
    
    The following objects are masked from 'package:base':
    
        intersect, setdiff, setequal, union
    
    x Method 'method_not_supported' is not part of the supported methods.
    i See `?spm_methods()`
    ! SSPM assumes areas are supplied in km^2
    ! SSPM assumes areas are supplied in km^2
    i  Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
    !  Warning: sspm is assuming WGS 84 CRS is to be used for casting
    i  Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
    !  Warning: sspm is assuming WGS 84 CRS is to be used for casting
    i  Discretizing using method tesselate_voronoi
    i  Discretizing using method tesselate_voronoi
    i  Discretizing using method tesselate_voronoi
    i  Discretizing using method tesselate_voronoi
    i  Discretizing using method tesselate_voronoi
    i  Discretizing using method Custom
    i  Discretizing using method triangulate_delaunay
    i  Discretizing using method triangulate_delaunay
    i  Discretizing using method triangulate_delaunay
    i  Discretizing using method triangulate_delaunay
    x Method 'wrong_method' is not part of the supported methods.
    i See `?spm_methods()`
    i  Fitting formula: weight_per_km2 ~ sfa + smooth_time(by = sfa) + smooth_space() + smooth_space_time(k = c(NA, 30)) for dataset ''
    i  Note:  response variable temp_at_bottom is NOT a biomass density variable
    i  Fitting formula: temp_at_bottom ~ smooth_time(by = sfa) + smooth_space() + smooth_space_time(k = c(NA, 30)) for dataset ''
    x Method 'method_not_supported' is not part of the supported methods.
    i See `?spm_methods()`
    i See `?spm_smooth_methods()`
    i  Joining smoothed data from all datasets
    [ FAIL 1 | WARN 0 | SKIP 1 | PASS 179 ]
    
    ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
    • On CRAN (1): 'test-zzzintegration.R:5:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Failure ('test-fit.R:15:3'): Fitting works as expected ──────────────────────
    `intercept` (`actual`) not equal to 7.855774 (`expected`).
    
      `actual`: 7.925
    `expected`: 7.856
    
    [ FAIL 1 | WARN 0 | SKIP 1 | PASS 179 ]
    Error: Test failures
    Execution halted

Package: tf
Check: Rd cross-references
New result: WARNING
  Missing link(s) in Rd file 'tfb_spline.Rd':
    ‘[mgcv:gam.fit]{mgcv::gam.fit()}’
  
  See section 'Cross-references' in the 'Writing R Extensions' manual.