Error(s) in re-building vignettes:
  ...
--- re-building 'getting-started.Rmd' using rmarkdown
Loading required package: parsnip
Loading required package: rugarch
Loading required package: parallel

Attaching package: 'rugarch'

The following object is masked from 'package:stats':

    sigma

Loading required package: rmgarch
-- Attaching packages -------------------------------------- tidymodels 0.1.4 --
v broom        0.7.9      v rsample      0.1.0 
v dials        0.0.10     v tibble       3.1.5 
v dplyr        1.0.7      v tidyr        1.1.4 
v ggplot2      3.3.5      v tune         0.1.6 
v infer        1.0.0      v workflows    0.2.4 
v modeldata    0.1.1      v workflowsets 0.1.0 
v purrr        0.3.4      v yardstick    0.0.8 
v recipes      0.1.17     
-- Conflicts ----------------------------------------- tidymodels_conflicts() --
x purrr::discard() masks scales::discard()
x dplyr::filter()  masks stats::filter()
x dplyr::first()   masks rmgarch::first()
x dplyr::lag()     masks stats::lag()
x dplyr::last()    masks rmgarch::last()
x purrr::reduce()  masks rugarch::reduce()
x recipes::step()  masks stats::step()
* Use tidymodels_prefer() to resolve common conflicts.
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
Found more than one class "atomicVector" in cache; using the first, from namespace 'Matrix'
Also defined by 'Rmpfr'
-- Attaching packages --------------------------------------- tidyverse 1.3.1 --
v readr   2.0.2     v forcats 0.5.1
v stringr 1.4.0     
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x readr::col_factor() masks scales::col_factor()
x purrr::discard()    masks scales::discard()
x dplyr::filter()     masks stats::filter()
x dplyr::first()      masks rmgarch::first()
x stringr::fixed()    masks recipes::fixed()
x dplyr::lag()        masks stats::lag()
x dplyr::last()       masks rmgarch::last()
x purrr::reduce()     masks rugarch::reduce()
x readr::spec()       masks yardstick::spec()
.date_var is missing. Using: date
frequency = 5 observations per 1 week
frequency = 5 observations per 1 week
.date_var is missing. Using: date
New names:
* V1 -> V1...1
* V1 -> V1...2
New names:
* V1 -> V1...3
New names:
* V1 -> V1...1
* V1 -> V1...2
New names:
* V1 -> V1...3
Warning in if (mmodel$model == "VAR") { :
  the condition has length > 1 and only the first element will be used
Warning in if (mmodel$model == "constant") { :
  the condition has length > 1 and only the first element will be used
--- finished re-building 'getting-started.Rmd'

--- re-building 'tuning_univariate_algorithms.Rmd' using rmarkdown
.date_var is missing. Using: date
i Slice1: preprocessor 1/1
v Slice1: preprocessor 1/1
i Slice1: preprocessor 1/1, model 1/5
frequency = 5 observations per 1 week
x Slice1: preprocessor 1/1, model 1/5: Error: 'data' does not have 4 columns

'dat...
i Slice1: preprocessor 1/1
v Slice1: preprocessor 1/1
i Slice1: preprocessor 1/1, model 2/5

 *** caught segfault ***
address 0x1, cause 'memory not mapped'

Traceback:
 1: gc(FALSE)
 2: system.time(res$fit <- eval_mod(fit_call, capture = control$verbosity ==     0, catch = control$catch, env = env, ...))
 3: form_form(object = object, env = env, control = control, ...)
 4: xy_form(object = object, env = eval_env, control = control, ...)
 5: fit_xy.model_spec(spec, x = mold$predictors, y = mold$outcomes,     control = control_parsnip)
 6: fit_xy(spec, x = mold$predictors, y = mold$outcomes, control = control_parsnip)
 7: fit_from_xy(spec, mold, control_parsnip)
 8: fit.action_model(action_model, workflow = workflow, control = control)
 9: fit(action_model, workflow = workflow, control = control)
10: .fit_model(workflow, control_workflow)
11: withCallingHandlers(warning = add_cond, expr)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             call <- sys.call(-4L)        dcall <- deparse(call, nlines = 1L)        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }    else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
16: try(withCallingHandlers(warning = add_cond, expr), silent = TRUE)
17: catcher(expr)
18: catch_and_log_fit(expr = .fit_model(workflow, control_workflow),     control, split, iter_msg_model, notes = out_notes)
19: fn(...)
20: doTryCatch(return(expr), name, parentenv, handler)
21: tryCatchOne(expr, names, parentenv, handlers[[1L]])
22: tryCatchList(expr, classes, parentenv, handlers)
23: tryCatch(expr = list(result = fn(...), error = NULL, warnings = warnings),     error = handle_error)
24: withCallingHandlers(expr = tryCatch(expr = list(result = fn(...),     error = NULL, warnings = warnings), error = handle_error),     warning = handle_warning)
25: safely_iterate(iteration, resamples, grid_info, workflow, metrics,     control, seed)
26: (function (fn, iteration, resamples, grid_info, workflow, metrics,     control, seed) {    safely_iterate <- super_safely(fn)    result <- safely_iterate(iteration, resamples, grid_info,         workflow, metrics, control, seed)    error <- result$error    warnings <- result$warnings    result <- result$result    if (is.null(error) && length(warnings) == 0L) {        return(result)    }    if (is.null(error)) {        res <- result        notes <- result$.notes    }    else {        res <- error        notes <- NULL    }    problems <- list(res = res, signals = warnings)    split <- resamples$splits[[iteration]]    notes <- log_problems(notes, control, split, "internal",         problems)    if (!is.null(error)) {        result <- list(.metrics = NULL, .extracts = NULL, .predictions = NULL,             .all_outcome_names = list(), .notes = NULL)    }    result[[".notes"]] <- notes    result})(fn = fn, ...)
27: eval_tidy(call, mask)
28: tune_grid_loop_iter_safely(iteration = iteration, resamples = resamples,     grid_info = grid_info_row, workflow = workflow, metrics = metrics,     control = control, seed = seed)
29: eval(xpr, envir = envir)
30: eval(xpr, envir = envir)
31: doTryCatch(return(expr), name, parentenv, handler)
32: tryCatchOne(expr, names, parentenv, handlers[[1L]])
33: tryCatchList(expr, classes, parentenv, handlers)
34: tryCatch(eval(xpr, envir = envir), error = function(e) e)
35: doTryCatch(return(expr), name, parentenv, handler)
36: tryCatchOne(expr, names, parentenv, handlers[[1L]])
37: tryCatchList(expr, classes, parentenv, handlers)
38: tryCatch({    repeat {        args <- nextElem(it)        if (obj$verbose) {            cat(sprintf("evaluation # %d:\n", i))            print(args)        }        for (a in names(args)) assign(a, args[[a]], pos = envir,             inherits = FALSE)        r <- tryCatch(eval(xpr, envir = envir), error = function(e) e)        if (obj$verbose) {            cat("result of evaluating expression:\n")            print(r)        }        tryCatch(accumulator(list(r), i), error = function(e) {            cat("error calling combine function:\n")            print(e)            NULL        })        i <- i + 1    }}, error = function(e) {    if (!identical(conditionMessage(e), "StopIteration"))         stop(simpleError(conditionMessage(e), expr))})
39: e$fun(obj, substitute(ex), parent.frame(), e$data)
40: foreach::foreach(iteration = iterations, .packages = packages,     .errorhandling = "pass") %:% foreach::foreach(row = rows,     seed = slice_seeds(seeds, iteration, n_grid_info), .packages = packages,     .errorhandling = "pass", .combine = iter_combine) %op% {    grid_info_row <- vctrs::vec_slice(grid_info, row)    tune_grid_loop_iter_safely(iteration = iteration, resamples = resamples,         grid_info = grid_info_row, workflow = workflow, metrics = metrics,         control = control, seed = seed)}
41: withCallingHandlers(expr, packageStartupMessage = function(c) tryInvokeRestart("muffleMessage"))
42: suppressPackageStartupMessages(results <- foreach::foreach(iteration = iterations,     .packages = packages, .errorhandling = "pass") %:% foreach::foreach(row = rows,     seed = slice_seeds(seeds, iteration, n_grid_info), .packages = packages,     .errorhandling = "pass", .combine = iter_combine) %op% {    grid_info_row <- vctrs::vec_slice(grid_info, row)    tune_grid_loop_iter_safely(iteration = iteration, resamples = resamples,         grid_info = grid_info_row, workflow = workflow, metrics = metrics,         control = control, seed = seed)})
43: tune_grid_loop(resamples = resamples, grid = grid, workflow = workflow,     metrics = metrics, control = control, rng = rng)
44: tune_grid_workflow(object, resamples = resamples, grid = grid,     metrics = metrics, pset = param_info, control = control)
45: tune_grid.workflow(object = wflw, resamples = resamples, param_info = parameters(wflw),     grid = 5, control = control_grid(verbose = TRUE, allow_par = TRUE,         parallel_over = "everything"))
46: tune_grid(object = wflw, resamples = resamples, param_info = parameters(wflw),     grid = 5, control = control_grid(verbose = TRUE, allow_par = TRUE,         parallel_over = "everything"))
47: eval(expr, envir, enclos)
48: eval(expr, envir, enclos)
49: withVisible(eval(expr, envir, enclos))
50: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,     error = eHandler, message = mHandler)
51: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,     enclos)), warning = wHandler, error = eHandler, message = mHandler))
52: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,     envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
53: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,     debug = debug, last = i == length(out), use_try = stop_on_error !=         2L, keep_warning = keep_warning, keep_message = keep_message,     output_handler = output_handler, include_timing = include_timing)
54: evaluate::evaluate(...)
55: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),     keep_message = !isFALSE(options$message), stop_on_error = if (is.numeric(options$error)) options$error else {        if (options$error && options$include)             0L        else 2L    }, output_handler = knit_handlers(options$render, options))
56: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),     stop_on_error = if (is.numeric(options$error)) options$error else {        if (options$error && options$include)             0L        else 2L    }, output_handler = knit_handlers(options$render, options)))
57: eng_r(options)
58: block_exec(params)
59: call_block(x)
60: process_group.block(group)
61: process_group(group)
62: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),     error = function(e) {        setwd(wd)        cat(res, sep = "\n", file = output %n% "")        message("Quitting from lines ", paste(current_lines(i),             collapse = "-"), " (", knit_concord$get("infile"),             ") ")    })
63: process_file(text, output)
64: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
65: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),     output_dir = getwd(), ...)
66: vweave_rmarkdown(...)
67: engine$weave(file, quiet = quiet, encoding = enc)
68: doTryCatch(return(expr), name, parentenv, handler)
69: tryCatchOne(expr, names, parentenv, handlers[[1L]])
70: tryCatchList(expr, classes, parentenv, handlers)
71: tryCatch({    engine$weave(file, quiet = quiet, encoding = enc)    setwd(startdir)    output <- find_vignette_product(name, by = "weave", engine = engine)    if (!have.makefile && vignette_is_tex(output)) {        texi2pdf(file = output, clean = FALSE, quiet = quiet)        output <- find_vignette_product(name, by = "texi2pdf",             engine = engine)    }    outputs <- c(outputs, output)}, error = function(e) {    thisOK <<- FALSE    fails <<- c(fails, file)    message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",         file, conditionMessage(e)))})
72: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/garchmodels.Rcheck/vign_test/garchmodels")
An irrecoverable exception occurred. R is aborting now ...
Segmentation fault