Package: brulee
Check: whether the namespace can be unloaded cleanly
New result: WARNING
  
   *** caught segfault ***
  address 0x2d180, cause 'memory not mapped'
  
  Traceback:
   1: cpp_lantern_init(file.path(torch_install_path(), "lib"))
   2: lantern_start()
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5: tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch({    lantern_start()    cpp_set_lantern_allocator(getOption("torch.threshold_call_gc",         4000L))    cpp_set_cuda_allocator_allocator_thresholds(getOption("torch.cuda_allocator_reserved_rate",         0.2), getOption("torch.cuda_allocator_allocated_rate",         0.8), getOption("torch.cuda_allocator_allocated_reserved_rate",         0.8))    register_lambda_function_deleter()    .generator_null <<- torch_generator()    .generator_null$set_current_seed(seed = sample(1e+05, 1))    .compilation_unit <<- cpp_jit_compilation_unit()}, error = function(e) {    msg <- if (is.character(e$message))         e$message    else "Unknown error."    cli::cli_warn(c(i = "torch failed to start, restart your R session to try again.",         i = "You might need to reinstall torch using {.fn install_torch}",         x = msg), parent = e)    FALSE})
   7: fun(libname, pkgname)
   8: doTryCatch(return(expr), name, parentenv, handler)
   9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  10: tryCatchList(expr, classes, parentenv, handlers)
  11: tryCatch(fun(libname, pkgname), error = identity)
  12: runHook(".onLoad", env, package.lib, package)
  13: loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]])
  14: namespaceImport(ns, loadNamespace(i, c(lib.loc, .libPaths()),     versionCheck = vI[[i]]), from = package)
  15: loadNamespace("brulee")
  16: withCallingHandlers(expr, message = function(c) if (inherits(c,     classes)) tryInvokeRestart("muffleMessage"))
  17: suppressMessages(loadNamespace("brulee"))
  An irrecoverable exception occurred. R is aborting now ...
  Segmentation fault

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

Package: lambdaTS
Check: whether the package can be loaded with stated dependencies
New result: WARNING
  
   *** caught segfault ***
  address 0x45f26, cause 'memory not mapped'
  
  Traceback:
   1: dyn.load(file, DLLpath = DLLpath, ...)
   2: library.dynam(lib, package, package.lib)
   3: loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])
   4: asNamespace(ns)
   5: namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc,     .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)
   6: loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])
   7: asNamespace(ns)
   8: namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc,     .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)
   9: loadNamespace(package, lib.loc)
  10: doTryCatch(return(expr), name, parentenv, handler)
  11: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  12: tryCatchList(expr, classes, parentenv, handlers)
  13: tryCatch({    ns <- loadNamespace(package, lib.loc)    env <- attachNamespace(ns, pos = pos, deps, exclude, include.only)}, error = function(e) {    P <- if (!is.null(cc <- conditionCall(e)))         paste(" in", deparse(cc)[1L])    else ""    msg <- gettextf("package or namespace load failed for %s%s:\n %s",         sQuote(package), P, conditionMessage(e))    if (logical.return && !quietly)         message(paste("Error:", msg), domain = NA)    else stop(msg, call. = FALSE, domain = NA)})
  14: library(lambdaTS)
  An irrecoverable exception occurred. R is aborting now ...
  Segmentation fault
  
  It looks like this package (or one of its dependent packages) has an
  unstated dependence on a standard package.  All dependencies must be
  declared in DESCRIPTION.
  See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
  manual.

Package: luz
Check: whether the package can be unloaded cleanly
New result: WARNING
  
   *** caught bus error ***
  address 0x7f902a8a89e8, cause 'non-existent physical address'
  
  Traceback:
   1: cpp_lantern_init(file.path(torch_install_path(), "lib"))
   2: lantern_start()
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5: tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch({    lantern_start()    cpp_set_lantern_allocator(getOption("torch.threshold_call_gc",         4000L))    cpp_set_cuda_allocator_allocator_thresholds(getOption("torch.cuda_allocator_reserved_rate",         0.2), getOption("torch.cuda_allocator_allocated_rate",         0.8), getOption("torch.cuda_allocator_allocated_reserved_rate",         0.8))    register_lambda_function_deleter()    .generator_null <<- torch_generator()    .generator_null$set_current_seed(seed = sample(1e+05, 1))    .compilation_unit <<- cpp_jit_compilation_unit()}, error = function(e) {    msg <- if (is.character(e$message))         e$message    else "Unknown error."    cli::cli_warn(c(i = "torch failed to start, restart your R session to try again.",         i = "You might need to reinstall torch using {.fn install_torch}",         x = msg), parent = e)    FALSE})
   7: fun(libname, pkgname)
   8: doTryCatch(return(expr), name, parentenv, handler)
   9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  10: tryCatchList(expr, classes, parentenv, handlers)
  11: tryCatch(fun(libname, pkgname), error = identity)
  12: runHook(".onLoad", env, package.lib, package)
  13: loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]])
  14: namespaceImport(ns, loadNamespace(i, c(lib.loc, .libPaths()),     versionCheck = vI[[i]]), from = package)
  15: loadNamespace(package, lib.loc)
  16: doTryCatch(return(expr), name, parentenv, handler)
  17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  18: tryCatchList(expr, classes, parentenv, handlers)
  19: tryCatch({    ns <- loadNamespace(package, lib.loc)    env <- attachNamespace(ns, pos = pos, deps, exclude, include.only)}, error = function(e) {    P <- if (!is.null(cc <- conditionCall(e)))         paste(" in", deparse(cc)[1L])    else ""    msg <- gettextf("package or namespace load failed for %s%s:\n %s",         sQuote(package), P, conditionMessage(e))    if (logical.return && !quietly)         message(paste("Error:", msg), domain = NA)    else stop(msg, call. = FALSE, domain = NA)})
  20: library(luz)
  21: withCallingHandlers(expr, message = function(c) if (inherits(c,     classes)) tryInvokeRestart("muffleMessage"))
  22: suppressMessages(library(luz))
  An irrecoverable exception occurred. R is aborting now ...
  Bus error

Package: madgrad
Check: for code/documentation mismatches
New result: WARNING
  
   *** caught bus error ***
  address 0x7fd97c6a89e8, cause 'non-existent physical address'
  
  Traceback:
   1: cpp_lantern_init(file.path(torch_install_path(), "lib"))
   2: lantern_start()
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5: tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch({    lantern_start()    cpp_set_lantern_allocator(getOption("torch.threshold_call_gc",         4000L))    cpp_set_cuda_allocator_allocator_thresholds(getOption("torch.cuda_allocator_reserved_rate",         0.2), getOption("torch.cuda_allocator_allocated_rate",         0.8), getOption("torch.cuda_allocator_allocated_reserved_rate",         0.8))    register_lambda_function_deleter()    .generator_null <<- torch_generator()    .generator_null$set_current_seed(seed = sample(1e+05, 1))    .compilation_unit <<- cpp_jit_compilation_unit()}, error = function(e) {    msg <- if (is.character(e$message))         e$message    else "Unknown error."    cli::cli_warn(c(i = "torch failed to start, restart your R session to try again.",         i = "You might need to reinstall torch using {.fn install_torch}",         x = msg), parent = e)    FALSE})
   7: fun(libname, pkgname)
   8: doTryCatch(return(expr), name, parentenv, handler)
   9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  10: tryCatchList(expr, classes, parentenv, handlers)
  11: tryCatch(fun(libname, pkgname), error = identity)
  12: runHook(".onLoad", env, package.lib, package)
  13: loadNamespace(name)
  14: doTryCatch(return(expr), name, parentenv, handler)
  15: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  16: tryCatchList(expr, classes, parentenv, handlers)
  17: tryCatch(loadNamespace(name), error = function(e) {    tr <- Sys.getenv("_R_NO_REPORT_MISSING_NAMESPACES_")    if (tr == "false" || (where != "<unknown>" && !nzchar(tr))) {        warning(gettextf("namespace %s is not available and has been replaced\nby .GlobalEnv when processing object %s",             sQuote(name)[1L], sQuote(where)), domain = NA, call. = FALSE,             immediate. = TRUE)        if (nzchar(Sys.getenv("_R_CALLS_MISSING_NAMESPACES_")))             print(sys.calls())    }    .GlobalEnv})
  18: .Internal(getRegisteredNamespace(name)) %||% tryCatch(loadNamespace(name),     error = function(e) {        tr <- Sys.getenv("_R_NO_REPORT_MISSING_NAMESPACES_")        if (tr == "false" || (where != "<unknown>" && !nzchar(tr))) {            warning(gettextf("namespace %s is not available and has been replaced\nby .GlobalEnv when processing object %s",                 sQuote(name)[1L], sQuote(where)), domain = NA,                 call. = FALSE, immediate. = TRUE)            if (nzchar(Sys.getenv("_R_CALLS_MISSING_NAMESPACES_")))                 print(sys.calls())        }        .GlobalEnv    })
  19: ..getNamespace(c("torch", "0.16.0"), "<unknown>")
  20: (function (n) {    if (existsInFrame(n, envenv))         envenv[[n]]    else {        e <- mkenv()        envenv[[n]] <- e        key <- env[[n]]        ekey <- if (is.list(key))             key$eagerKey        else key        data <- lazyLoadDBfetch(ekey, datafile, compressed, envhook)        parent.env(e) <- data$enclos %||% emptyenv()        list2env(data$bindings, e)        if (!is.null(data$attributes))             attributes(e) <- data$attributes        if (!is.null(data$isS4) && data$isS4)             .Internal(setS4Object(e, TRUE, TRUE))        if (is.list(key)) {            expr <- quote(lazyLoadDBfetch(KEY, datafile, compressed,                 envhook))            .Internal(makeLazy(names(key$lazyKeys), key$lazyKeys,                 expr, parent.env(environment()), e))        }        if (!is.null(data$locked) && data$locked)             .Internal(lockEnvironment(e, FALSE))        e    }})("env::7")
  21: (function (n) {    if (existsInFrame(n, envenv))         envenv[[n]]    else {        e <- mkenv()        envenv[[n]] <- e        key <- env[[n]]        ekey <- if (is.list(key))             key$eagerKey        else key        data <- lazyLoadDBfetch(ekey, datafile, compressed, envhook)        parent.env(e) <- data$enclos %||% emptyenv()        list2env(data$bindings, e)        if (!is.null(data$attributes))             attributes(e) <- data$attributes        if (!is.null(data$isS4) && data$isS4)             .Internal(setS4Object(e, TRUE, TRUE))        if (is.list(key)) {            expr <- quote(lazyLoadDBfetch(KEY, datafile, compressed,                 envhook))            .Internal(makeLazy(names(key$lazyKeys), key$lazyKeys,                 expr, parent.env(environment()), e))        }        if (!is.null(data$locked) && data$locked)             .Internal(lockEnvironment(e, FALSE))        e    }})("env::6")
  22: (function (n) {    if (existsInFrame(n, envenv))         envenv[[n]]    else {        e <- mkenv()        envenv[[n]] <- e        key <- env[[n]]        ekey <- if (is.list(key))             key$eagerKey        else key        data <- lazyLoadDBfetch(ekey, datafile, compressed, envhook)        parent.env(e) <- data$enclos %||% emptyenv()        list2env(data$bindings, e)        if (!is.null(data$attributes))             attributes(e) <- data$attributes        if (!is.null(data$isS4) && data$isS4)             .Internal(setS4Object(e, TRUE, TRUE))        if (is.list(key)) {            expr <- quote(lazyLoadDBfetch(KEY, datafile, compressed,                 envhook))            .Internal(makeLazy(names(key$lazyKeys), key$lazyKeys,                 expr, parent.env(environment()), e))        }        if (!is.null(data$locked) && data$locked)             .Internal(lockEnvironment(e, FALSE))        e    }})("env::5")
  23: get(f, envir = code_env)
  24: FUN(X[[i]], ...)
  25: lapply(x, f)
  26: unlist(lapply(x, f))
  27: Filter(function(f) {    f <- get(f, envir = code_env)    typeof(f) == "closure"}, objects_in_code)
  28: tools::codoc(package = "madgrad")
  An irrecoverable exception occurred. R is aborting now ...
  Bus error

Package: RGAN
Check: R code for possible problems
New result: NOTE
  
   *** caught bus error ***
  address 0x7ff24fbb83b0, cause 'non-existent physical address'
  
  Traceback:
   1: cpp_lantern_init(file.path(torch_install_path(), "lib"))
   2: lantern_start()
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5: tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch({    lantern_start()    cpp_set_lantern_allocator(getOption("torch.threshold_call_gc",         4000L))    cpp_set_cuda_allocator_allocator_thresholds(getOption("torch.cuda_allocator_reserved_rate",         0.2), getOption("torch.cuda_allocator_allocated_rate",         0.8), getOption("torch.cuda_allocator_allocated_reserved_rate",         0.8))    register_lambda_function_deleter()    .generator_null <<- torch_generator()    .generator_null$set_current_seed(seed = sample(1e+05, 1))    .compilation_unit <<- cpp_jit_compilation_unit()}, error = function(e) {    msg <- if (is.character(e$message))         e$message    else "Unknown error."    cli::cli_warn(c(i = "torch failed to start, restart your R session to try again.",         i = "You might need to reinstall torch using {.fn install_torch}",         x = msg), parent = e)    FALSE})
   7: fun(libname, pkgname)
   8: doTryCatch(return(expr), name, parentenv, handler)
   9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  10: tryCatchList(expr, classes, parentenv, handlers)
  11: tryCatch(fun(libname, pkgname), error = identity)
  12: runHook(".onLoad", env, package.lib, package)
  13: loadNamespace(name)
  14: doTryCatch(return(expr), name, parentenv, handler)
  15: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  16: tryCatchList(expr, classes, parentenv, handlers)
  17: tryCatch(loadNamespace(name), error = function(e) {    tr <- Sys.getenv("_R_NO_REPORT_MISSING_NAMESPACES_")    if (tr == "false" || (where != "<unknown>" && !nzchar(tr))) {        warning(gettextf("namespace %s is not available and has been replaced\nby .GlobalEnv when processing object %s",             sQuote(name)[1L], sQuote(where)), domain = NA, call. = FALSE,             immediate. = TRUE)        if (nzchar(Sys.getenv("_R_CALLS_MISSING_NAMESPACES_")))             print(sys.calls())    }    .GlobalEnv})
  18: .Internal(getRegisteredNamespace(name)) %||% tryCatch(loadNamespace(name),     error = function(e) {        tr <- Sys.getenv("_R_NO_REPORT_MISSING_NAMESPACES_")        if (tr == "false" || (where != "<unknown>" && !nzchar(tr))) {            warning(gettextf("namespace %s is not available and has been replaced\nby .GlobalEnv when processing object %s",                 sQuote(name)[1L], sQuote(where)), domain = NA,                 call. = FALSE, immediate. = TRUE)            if (nzchar(Sys.getenv("_R_CALLS_MISSING_NAMESPACES_")))                 print(sys.calls())        }        .GlobalEnv    })
  19: ..getNamespace(c("torch", "0.16.0"), "<unknown>")
  20: (function (n) {    if (existsInFrame(n, envenv))         envenv[[n]]    else {        e <- mkenv()        envenv[[n]] <- e        key <- env[[n]]        ekey <- if (is.list(key))             key$eagerKey        else key        data <- lazyLoadDBfetch(ekey, datafile, compressed, envhook)        parent.env(e) <- data$enclos %||% emptyenv()        list2env(data$bindings, e)        if (!is.null(data$attributes))             attributes(e) <- data$attributes        if (!is.null(data$isS4) && data$isS4)             .Internal(setS4Object(e, TRUE, TRUE))        if (is.list(key)) {            expr <- quote(lazyLoadDBfetch(KEY, datafile, compressed,                 envhook))            .Internal(makeLazy(names(key$lazyKeys), key$lazyKeys,                 expr, parent.env(environment()), e))        }        if (!is.null(data$locked) && data$locked)             .Internal(lockEnvironment(e, FALSE))        e    }})("env::7")
  21: (function (n) {    if (existsInFrame(n, envenv))         envenv[[n]]    else {        e <- mkenv()        envenv[[n]] <- e        key <- env[[n]]        ekey <- if (is.list(key))             key$eagerKey        else key        data <- lazyLoadDBfetch(ekey, datafile, compressed, envhook)        parent.env(e) <- data$enclos %||% emptyenv()        list2env(data$bindings, e)        if (!is.null(data$attributes))             attributes(e) <- data$attributes        if (!is.null(data$isS4) && data$isS4)             .Internal(setS4Object(e, TRUE, TRUE))        if (is.list(key)) {            expr <- quote(lazyLoadDBfetch(KEY, datafile, compressed,                 envhook))            .Internal(makeLazy(names(key$lazyKeys), key$lazyKeys,                 expr, parent.env(environment()), e))        }        if (!is.null(data$locked) && data$locked)             .Internal(lockEnvironment(e, FALSE))        e    }})("env::18")
  22: (function (n) {    if (existsInFrame(n, envenv))         envenv[[n]]    else {        e <- mkenv()        envenv[[n]] <- e        key <- env[[n]]        ekey <- if (is.list(key))             key$eagerKey        else key        data <- lazyLoadDBfetch(ekey, datafile, compressed, envhook)        parent.env(e) <- data$enclos %||% emptyenv()        list2env(data$bindings, e)        if (!is.null(data$attributes))             attributes(e) <- data$attributes        if (!is.null(data$isS4) && data$isS4)             .Internal(setS4Object(e, TRUE, TRUE))        if (is.list(key)) {            expr <- quote(lazyLoadDBfetch(KEY, datafile, compressed,                 envhook))            .Internal(makeLazy(names(key$lazyKeys), key$lazyKeys,                 expr, parent.env(environment()), e))        }        if (!is.null(data$locked) && data$locked)             .Internal(lockEnvironment(e, FALSE))        e    }})("env::17")
  23: as.list.environment(env, all.names = TRUE, sorted = TRUE)
  24: as.list(env, all.names = TRUE, sorted = TRUE)
  25: find_bad_closures(code_env)
  26: tools:::.check_depdef(package = "RGAN", WINDOWS = FALSE)
  An irrecoverable exception occurred. R is aborting now ...
  Bus error

Package: SEMdeep
Check: whether the package can be loaded with stated dependencies
New result: WARNING
  Loading required package: SEMgraph
  Loading required package: igraph
  Loading required package: methods
  
  Attaching package: ‘igraph’
  
  The following object is masked from ‘package:base’:
  
      union
  
  Loading required package: lavaan
  This is lavaan 0.6-19
  lavaan is FREE software! Please report any bugs.
  
  Attaching package: ‘SEMgraph’
  
  The following object is masked from ‘package:lavaan’:
  
      parameterEstimates
  
  
   *** caught segfault ***
  address 0x2d190, cause 'memory not mapped'
  
  Traceback:
   1: cpp_torch_generator()
   2: methods$initialize(NULL, NULL, ...)
   3: Generator$new()
   4: torch_generator()
   5: doTryCatch(return(expr), name, parentenv, handler)
   6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   7: tryCatchList(expr, classes, parentenv, handlers)
   8: tryCatch({    lantern_start()    cpp_set_lantern_allocator(getOption("torch.threshold_call_gc",         4000L))    cpp_set_cuda_allocator_allocator_thresholds(getOption("torch.cuda_allocator_reserved_rate",         0.2), getOption("torch.cuda_allocator_allocated_rate",         0.8), getOption("torch.cuda_allocator_allocated_reserved_rate",         0.8))    register_lambda_function_deleter()    .generator_null <<- torch_generator()    .generator_null$set_current_seed(seed = sample(1e+05, 1))    .compilation_unit <<- cpp_jit_compilation_unit()}, error = function(e) {    msg <- if (is.character(e$message))         e$message    else "Unknown error."    cli::cli_warn(c(i = "torch failed to start, restart your R session to try again.",         i = "You might need to reinstall torch using {.fn install_torch}",         x = msg), parent = e)    FALSE})
   9: fun(libname, pkgname)
  10: doTryCatch(return(expr), name, parentenv, handler)
  11: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  12: tryCatchList(expr, classes, parentenv, handlers)
  13: tryCatch(fun(libname, pkgname), error = identity)
  14: runHook(".onLoad", env, package.lib, package)
  15: loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])
  16: asNamespace(ns)
  17: namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc,     .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)
  18: loadNamespace(package, lib.loc)
  19: doTryCatch(return(expr), name, parentenv, handler)
  20: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  21: tryCatchList(expr, classes, parentenv, handlers)
  22: tryCatch({    ns <- loadNamespace(package, lib.loc)    env <- attachNamespace(ns, pos = pos, deps, exclude, include.only)}, error = function(e) {    P <- if (!is.null(cc <- conditionCall(e)))         paste(" in", deparse(cc)[1L])    else ""    msg <- gettextf("package or namespace load failed for %s%s:\n %s",         sQuote(package), P, conditionMessage(e))    if (logical.return && !quietly)         message(paste("Error:", msg), domain = NA)    else stop(msg, call. = FALSE, domain = NA)})
  23: library(SEMdeep)
  An irrecoverable exception occurred. R is aborting now ...
  Segmentation fault
  
  It looks like this package (or one of its dependent packages) has an
  unstated dependence on a standard package.  All dependencies must be
  declared in DESCRIPTION.
  See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
  manual.

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

Package: spinner
Check: tests
New result: ERROR
    Running ‘testthat.R’ [359s/225s]
  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(spinner)
    > 
    > test_check("spinner")
    epoch:  10    Train loss:  0.7296587    Val loss:  0.7658037 
    epoch:  20    Train loss:  0.6312472    Val loss:  0.8938044 
    epoch:  30    Train loss:  0.7966303    Val loss:  0.7577983 
    early stop at epoch:  35    Train loss:  0.7187584    Val loss:  0.8340319 
    epoch:  10    Train loss:  0.7772701    Val loss:  0.522902 
    epoch:  20    Train loss:  0.7762595    Val loss:  0.6553538 
    epoch:  30    Train loss:  0.8441178    Val loss:  0.6257916 
    epoch:  40    Train loss:  0.7924368    Val loss:  0.6765536 
    early stop at epoch:  48    Train loss:  0.8264863    Val loss:  0.6216179 
    epoch:  10    Train loss:  0.619444    Val loss:  0.4355841 
    epoch:  20    Train loss:  0.7106468    Val loss:  0.5221128 
    epoch:  30    Train loss:  0.7101115    Val loss:  0.6332979 
    epoch:  40    Train loss:  0.6813248    Val loss:  0.7437328 
    epoch:  50    Train loss:  0.6665376    Val loss:  0.2543085 
    epoch:  60    Train loss:  0.6605672    Val loss:  0.6042317 
    early stop at epoch:  67    Train loss:  0.6442814    Val loss:  0.8981792 
    epoch:  10    Train loss:  0.7050684    Val loss:  0.1291418 
    epoch:  20    Train loss:  0.763199    Val loss:  0.110771 
    epoch:  30    Train loss:  0.7050818    Val loss:  0.1807121 
    epoch:  40    Train loss:  0.7658291    Val loss:  0.08044621 
    epoch:  50    Train loss:  0.7072362    Val loss:  0.12575 
    epoch:  60    Train loss:  0.7938306    Val loss:  0.0174212 
    epoch:  70    Train loss:  0.7374686    Val loss:  0.2247893 
    epoch:  80    Train loss:  0.7310981    Val loss:  0.1976047 
    epoch:  90    Train loss:  0.7156397    Val loss:  0.1985154 
    epoch:  100    Train loss:  0.7261627    Val loss:  0.06746987 
    time: 65.122 sec elapsed
    epoch:  10    Train loss:  0.8482533    Val loss:  0.7808273 
    epoch:  20    Train loss:  0.7843667    Val loss:  0.7722629 
    epoch:  30    Train loss:  0.8268018    Val loss:  0.7261776 
    epoch:  40    Train loss:  0.7526438    Val loss:  0.6383763 
    early stop at epoch:  49    Train loss:  0.8314447    Val loss:  0.7491331 
    epoch:  10    Train loss:  0.7253362    Val loss:  0.7813281 
    epoch:  20    Train loss:  0.7149699    Val loss:  0.7636326 
    epoch:  30    Train loss:  0.7335743    Val loss:  0.7338508 
    early stop at epoch:  34    Train loss:  0.6761243    Val loss:  0.7929927 
    epoch:  10    Train loss:  0.6933863    Val loss:  0.6721683 
    epoch:  20    Train loss:  0.6706426    Val loss:  0.8345279 
    epoch:  30    Train loss:  0.7205679    Val loss:  0.7347966 
    early stop at epoch:  38    Train loss:  0.6348966    Val loss:  0.8029219 
    epoch:  10    Train loss:  0.8269185    Val loss:  0.6528622 
    epoch:  20    Train loss:  0.7081278    Val loss:  0.6500619 
    epoch:  30    Train loss:  0.755761    Val loss:  0.798498 
    epoch:  40    Train loss:  0.6888255    Val loss:  0.7146953 
    epoch:  50    Train loss:  0.7767602    Val loss:  0.646698 
    epoch:  60    Train loss:  0.7186184    Val loss:  0.730222 
    epoch:  70    Train loss:  0.6903644    Val loss:  0.7449238 
    epoch:  80    Train loss:  0.7790839    Val loss:  0.5977558 
    epoch:  90    Train loss:  0.7214634    Val loss:  0.6243414 
    epoch:  100    Train loss:  0.7338631    Val loss:  0.6404924 
    time: 47.139 sec elapsed
    epoch:  10    Train loss:  0.2859265    Val loss:  0.2068727 
    epoch:  20    Train loss:  0.2984724    Val loss:  0.3236572 
    epoch:  30    Train loss:  0.270416    Val loss:  0.4357646 
    early stop at epoch:  30    Train loss:  0.270416    Val loss:  0.4357646 
    epoch:  10    Train loss:  0.3585813    Val loss:  0.3153649 
    epoch:  20    Train loss:  0.2972764    Val loss:  0.2153269 
    epoch:  30    Train loss:  0.3488365    Val loss:  0.4321858 
    early stop at epoch:  30    Train loss:  0.3488365    Val loss:  0.4321858 
    epoch:  10    Train loss:  0.2341588    Val loss:  0.2794321 
    epoch:  20    Train loss:  0.2065745    Val loss:  0.1932212 
    epoch:  30    Train loss:  0.2426571    Val loss:  0.1447038 
    early stop at epoch:  36    Train loss:  0.268917    Val loss:  0.4349511 
    epoch:  10    Train loss:  0.3582795    Val loss:  0.2833864 
    epoch:  20    Train loss:  0.2889148    Val loss:  0.4885568 
    epoch:  30    Train loss:  0.3235161    Val loss:  0.4665593 
    early stop at epoch:  30    Train loss:  0.3235161    Val loss:  0.4665593 
    time: 25.945 sec elapsed
    epoch:  10    Train loss:  0.6142692    Val loss:  0.534342 
    epoch:  20    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  30    Train loss:  0.6142692    Val loss:  0.5428722 
    epoch:  40    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  50    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  60    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  70    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  80    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  90    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  100    Train loss:  0.6142692    Val loss:  0.5131603 
    epoch:  10    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  20    Train loss:  0.7049654    Val loss:  0.6535779 
    epoch:  30    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  40    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  50    Train loss:  0.7049654    Val loss:  0.6494278 
    epoch:  60    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  70    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  80    Train loss:  0.7049654    Val loss:  0.652582 
    epoch:  90    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  100    Train loss:  0.7049654    Val loss:  0.6577361 
    epoch:  10    Train loss:  0.7025672    Val loss:  0.6688986 
    epoch:  20    Train loss:  0.7095096    Val loss:  0.6763295 
    epoch:  30    Train loss:  0.6872968    Val loss:  0.6763295 
    early stop at epoch:  31    Train loss:  0.6872968    Val loss:  0.7268884 
    time: 17.7 sec elapsed
    epoch:  10    Train loss:  0.07086422    Val loss:  0.1295418 
    epoch:  20    Train loss:  0.07086422    Val loss:  0.150863 
    epoch:  30    Train loss:  0.07086422    Val loss:  0.1370502 
    early stop at epoch:  30    Train loss:  0.07086422    Val loss:  0.1370502 
    epoch:  10    Train loss:  0.1184543    Val loss:  0.2327785 
    epoch:  20    Train loss:  0.1407779    Val loss:  0.1184543 
    epoch:  30    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  40    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  50    Train loss:  0.1184543    Val loss:  0.3209745 
    epoch:  60    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  70    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  80    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  90    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  100    Train loss:  0.1184543    Val loss:  0.1184543 
    epoch:  10    Train loss:  0.115987    Val loss:  0.3021982 
    epoch:  20    Train loss:  0.2227834    Val loss:  0.07547683 
    epoch:  30    Train loss:  0.222783    Val loss:  0.07168996 
    early stop at epoch:  39    Train loss:  0.07168997    Val loss:  0.3695349 
    time: 12.083 sec elapsed
    epoch:  10    Train loss:  0.8840502    Val loss:  0.7528918 
    epoch:  20    Train loss:  0.8840502    Val loss:  0.7386833 
    epoch:  30    Train loss:  0.8840502    Val loss:  0.8115932 
    epoch:  40    Train loss:  0.8840502    Val loss:  0.7679083 
    epoch:  50    Train loss:  0.8840502    Val loss:  0.7460439 
    epoch:  60    Train loss:  0.8840502    Val loss:  0.7271828 
    epoch:  70    Train loss:  0.8840502    Val loss:  0.7271828 
    epoch:  80    Train loss:  0.8840502    Val loss:  0.7627334 
    early stop at epoch:  87    Train loss:  0.8976642    Val loss:  0.7493047 
    epoch:  10    Train loss:  0.8616574    Val loss:  0.7825515 
    epoch:  20    Train loss:  0.8616574    Val loss:  0.7337028 
    epoch:  30    Train loss:  0.8496215    Val loss:  0.8155159 
    early stop at epoch:  37    Train loss:  0.8891432    Val loss:  0.7763019 
    epoch:  10    Train loss:  0.8155231    Val loss:  0.5132446 
    epoch:  20    Train loss:  0.8155231    Val loss:  0.5116741 
    epoch:  30    Train loss:  0.8290172    Val loss:  0.5206639 
    epoch:  40    Train loss:  0.8155231    Val loss:  0.6798337 
    epoch:  50    Train loss:  0.8155231    Val loss:  0.5661802 
    epoch:  60    Train loss:  0.8270854    Val loss:  0.5020163 
    epoch:  70    Train loss:  0.8317779    Val loss:  0.5016666 
    epoch:  80    Train loss:  0.8343502    Val loss:  0.5764117 
    epoch:  90    Train loss:  0.8241311    Val loss:  0.5016666 
    epoch:  100    Train loss:  0.8155231    Val loss:  0.5016666 
    time: 20.515 sec elapsed
    epoch:  10    Train loss:  0.6843129    Val loss:  0.9026864 
    epoch:  20    Train loss:  0.7129108    Val loss:  0.8759718 
    epoch:  30    Train loss:  0.6363723    Val loss:  0.9134344 
    early stop at epoch:  33    Train loss:  0.6145143    Val loss:  0.9189636 
    epoch:  10    Train loss:  0.9203368    Val loss:  0.8369982 
    epoch:  20    Train loss:  0.9103207    Val loss:  0.8256086 
    epoch:  30    Train loss:  0.9054757    Val loss:  0.8447726 
    early stop at epoch:  36    Train loss:  0.9308423    Val loss:  0.850892 
    epoch:  10    Train loss:  0.8853705    Val loss:  0.413904 
    epoch:  20    Train loss:  0.8903883    Val loss:  0.4568593 
    epoch:  30    Train loss:  0.8836291    Val loss:  0.4770255 
    epoch:  40    Train loss:  0.8990043    Val loss:  0.5015495 
    epoch:  50    Train loss:  0.872561    Val loss:  0.2868867 
    epoch:  60    Train loss:  0.8548701    Val loss:  0.4388559 
    epoch:  70    Train loss:  0.8721351    Val loss:  0.4282742 
    epoch:  80    Train loss:  0.9115008    Val loss:  0.432579 
    epoch:  90    Train loss:  0.898841    Val loss:  0.3531144 
    epoch:  100    Train loss:  0.8563035    Val loss:  0.3222978 
    time: 29.826 sec elapsed
    random search: 62.433 sec elapsed
    [ FAIL 1 | WARN 66 | SKIP 0 | PASS 46 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test.R:72:13'): Correct outcome format and size for base outcome3 ───
    <purrr_error_indexed/rlang_error/error/condition>
    Error in `purrr::pmap(hyper_params, ~spinner(graph, target, node_labels, 
        edge_labels, context_labels, direction = ..1, sampling = NA, 
        threshold = 0.01, method = ..2, node_embedding_size = ..13, 
        edge_embedding_size = ..14, context_embedding_size = ..15, 
        update_order = ..3, n_layers = ..4, skip_shortcut = ..5, 
        forward_layer = ..6, forward_activation = ..7, forward_drop = ..8, 
        mode = ..9, optimization = ..10, epochs, lr = ..11, patience, 
        weight_decay = ..12, reps, folds, holdout, verbose, seed))`: i In index: 2.
    Caused by error in `pmap()`:
    i In index: 1.
    Caused by error in `training_function()`:
    ! not enough data for training
    
    [ FAIL 1 | WARN 66 | SKIP 0 | PASS 46 ]
    Error: Test failures
    Execution halted

Package: spinner
Check: whether the namespace can be loaded with stated dependencies
New result: WARNING
  
   *** caught segfault ***
  address 0x45f26, cause 'memory not mapped'
  
  Traceback:
   1: dyn.load(file, DLLpath = DLLpath, ...)
   2: library.dynam(lib, package, package.lib)
   3: loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])
   4: asNamespace(ns)
   5: namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc,     .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)
   6: loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])
   7: asNamespace(ns)
   8: namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc,     .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)
   9: loadNamespace("spinner")
  10: eval(expr)
  11: eval(expr)
  12: withCallingHandlers(expr, message = function(c) if (inherits(c,     classes)) tryInvokeRestart("muffleMessage"))
  13: suppressMessages(eval(expr))
  14: withCallingHandlers(suppressMessages(eval(expr)), warning = .whandler)
  15: tools:::.load_namespace_rather_quietly("spinner")
  An irrecoverable exception occurred. R is aborting now ...
  Segmentation fault
  
  A namespace must be able to be loaded with just the base namespace
  loaded: otherwise if the namespace gets loaded by a saved object, the
  session will be unable to start.
  
  Probably some imports need to be declared in the NAMESPACE file.

Package: tabnet
Check: whether the package can be loaded
New result: ERROR
  Loading this package had a fatal error status code 135
  Loading log:
  
   *** caught bus error ***
  address 0x7faf61541f25, cause 'non-existent physical address'
  
  Traceback:
   1: cpp_lantern_init(file.path(torch_install_path(), "lib"))
   2: lantern_start()
   3: doTryCatch(return(expr), name, parentenv, handler)
   4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5: tryCatchList(expr, classes, parentenv, handlers)
   6: tryCatch({    lantern_start()    cpp_set_lantern_allocator(getOption("torch.threshold_call_gc",         4000L))    cpp_set_cuda_allocator_allocator_thresholds(getOption("torch.cuda_allocator_reserved_rate",         0.2), getOption("torch.cuda_allocator_allocated_rate",         0.8), getOption("torch.cuda_allocator_allocated_reserved_rate",         0.8))    register_lambda_function_deleter()    .generator_null <<- torch_generator()    .generator_null$set_current_seed(seed = sample(1e+05, 1))    .compilation_unit <<- cpp_jit_compilation_unit()}, error = function(e) {    msg <- if (is.character(e$message))         e$message    else "Unknown error."    cli::cli_warn(c(i = "torch failed to start, restart your R session to try again.",         i = "You might need to reinstall torch using {.fn install_torch}",         x = msg), parent = e)    FALSE})
   7: fun(libname, pkgname)
   8: doTryCatch(return(expr), name, parentenv, handler)
   9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  10: tryCatchList(expr, classes, parentenv, handlers)
  11: tryCatch(fun(libname, pkgname), error = identity)
  12: runHook(".onLoad", env, package.lib, package)
  13: loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])
  14: asNamespace(ns)
  15: namespaceImportFrom(ns, loadNamespace(j <- i[[1L]], c(lib.loc,     .libPaths()), versionCheck = vI[[j]]), i[[2L]], from = package)
  16: loadNamespace(package, lib.loc)
  17: doTryCatch(return(expr), name, parentenv, handler)
  18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  19: tryCatchList(expr, classes, parentenv, handlers)
  20: tryCatch({    ns <- loadNamespace(package, lib.loc)    env <- attachNamespace(ns, pos = pos, deps, exclude, include.only)}, error = function(e) {    P <- if (!is.null(cc <- conditionCall(e)))         paste(" in", deparse(cc)[1L])    else ""    msg <- gettextf("package or namespace load failed for %s%s:\n %s",         sQuote(package), P, conditionMessage(e))    if (logical.return && !quietly)         message(paste("Error:", msg), domain = NA)    else stop(msg, call. = FALSE, domain = NA)})
  21: library(tabnet)
  An irrecoverable exception occurred. R is aborting now ...
  Bus error