Error(s) in re-building vignettes:
  ...
--- re-building 'getting-started.rmd' using rmarkdown
 ----------- FAILURE REPORT -------------- 
 --- failure: the condition has length > 1 ---
 --- srcref --- 
: 
 --- package (from environment) --- 
ncvreg
 --- call from context --- 
ncvreg(X, y)
 --- call from argument --- 
if (class(X) != "matrix") {
    tmp <- try(X <- model.matrix(~0 + ., data = X), silent = TRUE)
    if (class(tmp)[1] == "try-error") 
        stop("X must be a matrix or able to be coerced to a matrix")
}
 --- R stacktrace ---
where 1: ncvreg(X, y)
where 2: eval(expr, envir, enclos)
where 3: eval(expr, envir, enclos)
where 4: withVisible(eval(expr, envir, enclos))
where 5: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler, 
    error = eHandler, message = mHandler)
where 6: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir, 
    enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 7: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr, 
    envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 8: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
    debug = debug, last = i == length(out), use_try = stop_on_error != 
        2L, keep_warning = keep_warning, keep_message = keep_message, 
    output_handler = output_handler, include_timing = include_timing)
where 9: evaluate::evaluate(...)
where 10: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$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 11: 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$render, options)))
where 12: block_exec(params)
where 13: call_block(x)
where 14: process_group.block(group)
where 15: process_group(group)
where 16: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 
    error = function(e) {
        setwd(wd)
        cat(res, sep = "\n", file = output %n% "")
        message("Quitting from lines ", paste(current_lines(i), 
            collapse = "-"), " (", knit_concord$get("infile"), 
            ") ")
    })
where 17: process_file(text, output)
where 18: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 19: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 
    ...)
where 20: vweave_rmarkdown(...)
where 21: engine$weave(file, quiet = quiet, encoding = enc)
where 22: doTryCatch(return(expr), name, parentenv, handler)
where 23: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 24: tryCatchList(expr, classes, parentenv, handlers)
where 25: tryCatch({
    engine$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 26: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/ncvreg.Rcheck/vign_test/ncvreg", 
    ser_elibs = "/tmp/RtmpbXyM3o/file4d3f1ffa8d37.rds")

 --- value of length: 2 type: logical ---
[1] FALSE  TRUE
 --- function from context --- 
function (X, y, family = c("gaussian", "binomial", "poisson"), 
    penalty = c("MCP", "SCAD", "lasso"), gamma = switch(penalty, 
        SCAD = 3.7, 3), alpha = 1, lambda.min = ifelse(n > p, 
        0.001, 0.05), nlambda = 100, lambda, eps = 1e-04, max.iter = 10000, 
    convex = TRUE, dfmax = p + 1, penalty.factor = rep(1, ncol(X)), 
    warn = TRUE, returnX, ...) 
{
    family <- match.arg(family)
    penalty <- match.arg(penalty)
    if (class(X) != "matrix") {
        tmp <- try(X <- model.matrix(~0 + ., data = X), silent = TRUE)
        if (class(tmp)[1] == "try-error") 
            stop("X must be a matrix or able to be coerced to a matrix")
    }
    if (storage.mode(X) == "integer") 
        storage.mode(X) <- "double"
    if (class(y) != "numeric") {
        tmp <- try(y <- as.numeric(y), silent = TRUE)
        if (class(tmp)[1] == "try-error") 
            stop("y must numeric or able to be coerced to numeric")
    }
    if (storage.mode(penalty.factor) != "double") 
        storage.mode(penalty.factor) <- "double"
    if (gamma <= 1 & penalty == "MCP") 
        stop("gamma must be greater than 1 for the MC penalty")
    if (gamma <= 2 & penalty == "SCAD") 
        stop("gamma must be greater than 2 for the SCAD penalty")
    if (nlambda < 2) 
        stop("nlambda must be at least 2")
    if (alpha <= 0) 
        stop("alpha must be greater than 0; choose a small positive number instead")
    if (any(is.na(y)) | any(is.na(X))) 
        stop("Missing data (NA's) detected.  Take actions (e.g., removing cases, removing features, imputation) to eliminate missing data before passing X and y to ncvreg")
    if (length(penalty.factor) != ncol(X)) 
        stop("penalty.factor does not match up with X")
    if (family == "binomial" & length(table(y)) > 2) 
        stop("Attemping to use family='binomial' with non-binary data")
    if (family == "binomial" & !identical(sort(unique(y)), 0:1)) 
        y <- as.numeric(y == max(y))
    if (length(y) != nrow(X)) 
        stop("X and y do not have the same number of observations")
    dots <- list(...)
    if ("n.lambda" %in% names(dots)) 
        nlambda <- dots$n.lambda
    XX <- std(X)
    ns <- attr(XX, "nonsingular")
    penalty.factor <- penalty.factor[ns]
    p <- ncol(XX)
    if (family == "gaussian") {
        yy <- y - mean(y)
    }
    else {
        yy <- y
    }
    n <- length(yy)
    if (missing(lambda)) {
        lambda <- setupLambda(XX, yy, family, alpha, lambda.min, 
            nlambda, penalty.factor)
        user.lambda <- FALSE
    }
    else {
        nlambda <- length(lambda)
        user.lambda <- TRUE
    }
    if (sys.nframe() > 1 && sys.call(-1)[[1]] == "local_mfdr") 
        return(list(X = XX, y = yy))
    if (family == "gaussian") {
        res <- .Call("cdfit_gaussian", XX, yy, penalty, lambda, 
            eps, as.integer(max.iter), as.double(gamma), penalty.factor, 
            alpha, as.integer(dfmax), as.integer(user.lambda | 
                any(penalty.factor == 0)))
        a <- rep(mean(y), nlambda)
        b <- matrix(res[[1]], p, nlambda)
        loss <- res[[2]]
        iter <- res[[3]]
    }
    else {
        if (family == "binomial") {
            res <- .Call("cdfit_binomial", XX, yy, penalty, lambda, 
                eps, as.integer(max.iter), as.double(gamma), 
                penalty.factor, alpha, as.integer(dfmax), as.integer(user.lambda | 
                  any(penalty.factor == 0)), as.integer(warn))
        }
        else if (family == "poisson") {
            res <- .Call("cdfit_poisson", XX, yy, penalty, lambda, 
                eps, as.integer(max.iter), as.double(gamma), 
                penalty.factor, alpha, as.integer(dfmax), as.integer(user.lambda | 
                  any(penalty.factor == 0)), as.integer(warn))
        }
        a <- res[[1]]
        b <- matrix(res[[2]], p, nlambda)
        loss <- res[[3]]
        Eta <- matrix(res[[4]], n, nlambda)
        iter <- res[[5]]
    }
    ind <- !is.na(iter)
    a <- a[ind]
    b <- b[, ind, drop = FALSE]
    iter <- iter[ind]
    lambda <- lambda[ind]
    loss <- loss[ind]
    if (family == "binomial") 
        Eta <- Eta[, ind]
    if (warn & sum(iter) == max.iter) 
        warning("Maximum number of iterations reached")
    convex.min <- if (convex) 
        convexMin(b, XX, penalty, gamma, lambda * (1 - alpha), 
            family, penalty.factor, a = a)
    else NULL
    beta <- matrix(0, nrow = (ncol(X) + 1), ncol = length(lambda))
    bb <- b/attr(XX, "scale")[ns]
    beta[ns + 1, ] <- bb
    beta[1, ] <- a - crossprod(attr(XX, "center")[ns], bb)
    varnames <- if (is.null(colnames(X))) 
        paste("V", 1:ncol(X), sep = "")
    else colnames(X)
    varnames <- c("(Intercept)", varnames)
    dimnames(beta) <- list(varnames, lamNames(lambda))
    val <- structure(list(beta = beta, iter = iter, lambda = lambda, 
        penalty = penalty, family = family, gamma = gamma, alpha = alpha, 
        convex.min = convex.min, loss = loss, penalty.factor = penalty.factor, 
        n = n), class = "ncvreg")
    if (family == "poisson") 
        val$y <- y
    if (family == "binomial") 
        val$Eta <- Eta
    if (missing(returnX)) {
        if (utils::object.size(XX) > 1e+08) {
            warning("Due to the large size of X (>100 Mb), returnX has been turned off.\nTo turn this message off, explicitly specify returnX=TRUE or returnX=FALSE).")
            returnX <- FALSE
        }
        else {
            returnX <- TRUE
        }
    }
    if (returnX) {
        val$X <- XX
        val$y <- yy
    }
    val
}
<bytecode: 0x1606630>
<environment: namespace:ncvreg>
 --- function search by body ---
Function ncvreg in namespace ncvreg has this body.
 ----------- END OF FAILURE REPORT -------------- 
Quitting from lines 44-45 (getting-started.rmd) 
Error: processing vignette 'getting-started.rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building 'getting-started.rmd'

SUMMARY: processing the following file failed:
  'getting-started.rmd'

Error: Vignette re-building failed.
Execution halted