Error(s) in re-building vignettes:
  ...
--- re-building 'caretEnsemble-intro.Rmd' using rmarkdown
Warning in train.default(x, y, weights = w, ...) :
  The metric "Accuracy" was not in the result set. ROC will be used instead.
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning: glm.fit: algorithm did not converge
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Warning in train.default(x, y, weights = w, ...) :
  The metric "Accuracy" was not in the result set. ROC will be used instead.
 ----------- FAILURE REPORT -------------- 
 --- failure: the condition has length > 1 ---
 --- srcref --- 
: 
 --- package (from environment) --- 
caretEnsemble
 --- call from context --- 
predict.caretList(model_list, newdata = head(testing))
 --- call from argument --- 
if (class(preds) != "matrix" & class(preds) != "data.frame") {
    if (class(preds) == "character" | class(preds) == "factor") {
        preds <- as.character(preds)
    }
    preds <- as.matrix(t(preds))
}
 --- R stacktrace ---
where 1: predict.caretList(model_list, newdata = head(testing))
where 2: predict(model_list, newdata = head(testing))
where 3: as.data.frame(predict(model_list, newdata = head(testing)))
where 4: eval(expr, envir, enclos)
where 5: eval(expr, envir, enclos)
where 6: withVisible(eval(expr, envir, enclos))
where 7: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler, 
    error = eHandler, message = mHandler)
where 8: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir, 
    enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 9: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr, 
    envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 10: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
    debug = debug, last = i == length(out), use_try = stop_on_error != 
        2L, keep_warning = keep_warning, keep_message = keep_message, 
    output_handler = output_handler, include_timing = include_timing)
where 11: evaluate::evaluate(...)
where 12: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning), 
    keep_message = !isFALSE(options$message), stop_on_error = if (options$error && 
        options$include) 0L else 2L, output_handler = knit_handlers(options$render, 
        options))
where 13: 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 (options$error && options$include) 0L else 2L, 
    output_handler = knit_handlers(options$render, options)))
where 14: block_exec(params)
where 15: call_block(x)
where 16: process_group.block(group)
where 17: process_group(group)
where 18: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 
    error = function(e) {
        setwd(wd)
        cat(res, sep = "\n", file = output %n% "")
        message("Quitting from lines ", paste(current_lines(i), 
            collapse = "-"), " (", knit_concord$get("infile"), 
            ") ")
    })
where 19: process_file(text, output)
where 20: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet, 
    encoding = encoding)
where 21: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 
    ...)
where 22: vweave_rmarkdown(...)
where 23: engine$weave(file, quiet = quiet, encoding = enc)
where 24: doTryCatch(return(expr), name, parentenv, handler)
where 25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 26: tryCatchList(expr, classes, parentenv, handlers)
where 27: tryCatch({
    engine$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)))
})
where 28: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/caretEnsemble.Rcheck/vign_test/caretEnsemble", 
    ser_elibs = "/tmp/RtmpL0qrEE/file695113526b3a.rds")

 --- value of length: 2 type: logical ---
[1] FALSE  TRUE
 --- function from context --- 
function (object, newdata = NULL, ..., verbose = FALSE) 
{
    if (is.null(newdata)) {
        warning("Predicting without new data is not well supported.  Attempting to predict on the training data.")
        newdata <- object[[1]]$trainingData
        if (is.null(newdata)) {
            stop("Could not find training data in the first model in the ensemble.")
        }
    }
    if (verbose == TRUE) {
        pboptions(type = "txt", char = "*")
    }
    else if (verbose == FALSE) {
        pboptions(type = "none")
    }
    preds <- pbsapply(object, function(x) {
        type <- x$modelType
        if (type == "Classification") {
            if (x$control$classProbs) {
                caret::predict.train(x, type = "prob", newdata = newdata, 
                  ...)[, 2]
            }
            else {
                caret::predict.train(x, type = "raw", newdata = newdata, 
                  ...)
            }
        }
        else if (type == "Regression") {
            caret::predict.train(x, type = "raw", newdata = newdata, 
                ...)
        }
        else {
            stop(paste("Unknown model type:", type))
        }
    })
    if (class(preds) != "matrix" & class(preds) != "data.frame") {
        if (class(preds) == "character" | class(preds) == "factor") {
            preds <- as.character(preds)
        }
        preds <- as.matrix(t(preds))
    }
    colnames(preds) <- make.names(sapply(object, function(x) x$method), 
        unique = TRUE)
    return(preds)
}
<bytecode: 0x15fca320>
<environment: namespace:caretEnsemble>
 --- function search by body ---
Function predict.caretList in namespace caretEnsemble has this body.
 ----------- END OF FAILURE REPORT -------------- 
Quitting from lines 60-62 (caretEnsemble-intro.Rmd) 
Error: processing vignette 'caretEnsemble-intro.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building 'caretEnsemble-intro.Rmd'

SUMMARY: processing the following file failed:
  'caretEnsemble-intro.Rmd'

Error: Vignette re-building failed.
Execution halted