Error(s) in re-building vignettes:
  ...
--- re-building 'dvmisc.Rmd' using rmarkdown
 ----------- FAILURE REPORT -------------- 
 --- failure: the condition has length > 1 ---
 --- srcref --- 
: 
 --- package (from environment) --- 
dvmisc
 --- call from context --- 
sumsim(estimates = cbind(MLE, s2), truth = 1)
 --- call from argument --- 
if (class(estimates) != "matrix") {
    estimates <- as.matrix(estimates)
}
 --- R stacktrace ---
where 1: sumsim(estimates = cbind(MLE, s2), truth = 1)
where 2: kable(sumsim(estimates = cbind(MLE, s2), truth = 1))
where 3: eval(expr, envir, enclos)
where 4: eval(expr, envir, enclos)
where 5: withVisible(eval(expr, envir, enclos))
where 6: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler, 
    error = eHandler, message = mHandler)
where 7: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir, 
    enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 8: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr, 
    envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 9: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
    debug = debug, last = i == length(out), use_try = stop_on_error != 
        2L, keep_warning = keep_warning, keep_message = keep_message, 
    output_handler = output_handler, include_timing = include_timing)
where 10: evaluate::evaluate(...)
where 11: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$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: 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$render, options)))
where 13: block_exec(params)
where 14: call_block(x)
where 15: process_group.block(group)
where 16: process_group(group)
where 17: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 
    error = function(e) {
        setwd(wd)
        cat(res, sep = "\n", file = output %n% "")
        message("Quitting from lines ", paste(current_lines(i), 
            collapse = "-"), " (", knit_concord$get("infile"), 
            ") ")
    })
where 18: process_file(text, output)
where 19: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet, 
    encoding = encoding)
where 20: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 
    ...)
where 21: vweave_rmarkdown(...)
where 22: engine$weave(file, quiet = quiet, encoding = enc)
where 23: doTryCatch(return(expr), name, parentenv, handler)
where 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 25: tryCatchList(expr, classes, parentenv, handlers)
where 26: tryCatch({
    engine$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 27: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/dvmisc.Rcheck/vign_test/dvmisc", 
    ser_elibs = "/tmp/RtmphM6z6i/file13a2848d00f.rds")

 --- value of length: 2 type: logical ---
[1] FALSE  TRUE
 --- function from context --- 
function (estimates, ses = NULL, truth = NULL, theta_0 = 0, statistics = c("mean_bias", 
    "sd", "mean_se", "mse", "coverage"), alpha = 0.05, digits = 3, 
    listwise_deletion = TRUE) 
{
    if (class(estimates) != "matrix") {
        estimates <- as.matrix(estimates)
    }
    if (!is.null(ses) && class(ses) != "matrix") {
        ses <- as.matrix(ses)
    }
    if (listwise_deletion) {
        if (is.null(ses)) {
            locs <- which(apply(estimates, 1, function(x) sum(is.na(x))) > 
                0)
            n.locs <- length(locs)
            if (n.locs > 0) {
                message(paste("Excluded", n.locs, "trial(s) due to missing value(s)"))
                estimates <- estimates[-locs, ]
            }
        }
        else {
            locs <- which(apply(estimates, 1, function(x) sum(is.na(x))) > 
                0 | apply(ses, 1, function(x) sum(is.na(x))) > 
                0)
            n.locs <- length(locs)
            if (n.locs > 0) {
                message(paste("Excluded", n.locs, "trial(s) due to missing value(s)"))
                estimates <- estimates[-locs, ]
                ses <- ses[-locs, ]
            }
        }
    }
    if (is.null(truth)) {
        statistics <- statistics[!statistics %in% c("mean-bias", 
            "median_bias", "mse", "coverage")]
    }
    if (is.null(ses)) {
        statistics <- statistics[!statistics %in% c("mean_se", 
            "coverage", "power")]
    }
    if (length(digits) == 1) {
        digits <- rep(digits, length(statistics))
    }
    mat <- matrix(NA, ncol = length(statistics), nrow = ncol(estimates))
    mat.colnames <- c()
    mat.rownames <- colnames(estimates)
    if (is.null(mat.rownames)) {
        mat.rownames <- paste("Method", 1:ncol(estimates))
    }
    if (any(c("coverage", "ci_width", "power") %in% statistics)) {
        zval <- qnorm(1 - (alpha/2))
    }
    index <- 0
    for (ii in 1:length(statistics)) {
        index <- index + 1
        statistic.ii <- statistics[ii]
        if (statistic.ii == "n") {
            mat[, index] <- apply(estimates, 2, function(x) sum(!is.na(x)))
            mat.colnames[ii] <- "N"
        }
        else if (statistic.ii == "mean") {
            mat[, index] <- round(apply(estimates, 2, function(x) mean(x, 
                na.rm = TRUE)), digits[ii])
            mat.colnames[ii] <- "Mean"
        }
        else if (statistic.ii == "median") {
            mat[, index] <- round(apply(estimates, 2, function(x) median(x, 
                na.rm = TRUE)), digits[ii])
            mat.colnames[ii] <- "Median"
        }
        else if (statistic.ii == "mean_bias") {
            mat[, index] <- round(apply(estimates, 2, function(x) mean(x, 
                na.rm = TRUE)) - truth, digits[ii])
            mat.colnames[ii] <- "Mean bias"
        }
        else if (statistic.ii == "median_bias") {
            mat[, index] <- round(apply(estimates, 2, function(x) median(x, 
                na.rm = TRUE)) - truth, digits[ii])
            mat.colnames[ii] <- "Median bias"
        }
        else if (statistic.ii == "sd") {
            mat[, index] <- round(apply(estimates, 2, function(x) sd(x, 
                na.rm = TRUE)), digits[ii])
            mat.colnames[ii] <- "SD"
        }
        else if (statistic.ii == "iqr") {
            mat[, index] <- round(apply(estimates, 2, function(x) IQR(x, 
                na.rm = TRUE)), digits[ii])
            mat.colnames[ii] <- "IQR"
        }
        else if (statistic.ii == "mean_se") {
            mat[, index] <- round(apply(ses, 2, function(x) mean(x, 
                na.rm = TRUE)), digits[ii])
            mat.colnames[ii] <- "Mean SE"
        }
        else if (statistic.ii == "mse") {
            mat[, index] <- round(apply(estimates, 2, function(x) mean((x - 
                truth)^2, na.rm = TRUE)), digits[ii])
            mat.colnames[ii] <- "MSE"
        }
        else if (statistic.ii == "coverage") {
            for (jj in 1:ncol(estimates)) {
                mat[jj, index] <- round(mean(inside(x = truth, 
                  ends = cbind(estimates[, jj] - zval * ses[, 
                    jj], estimates[, jj] + zval * ses[, jj])), 
                  na.rm = TRUE), digits[ii])
            }
            mat.colnames[ii] <- "Coverage"
        }
        else if (statistic.ii == "ci_width") {
            for (jj in 1:ncol(estimates)) {
                mat[jj, index] <- round(median((estimates[, jj] + 
                  zval * ses[, jj]) - (estimates[, jj] - zval * 
                  ses[, jj]), na.rm = TRUE), digits[ii])
            }
            mat.colnames[ii] <- "Median CI width"
        }
        else if (statistic.ii == "power") {
            for (jj in 1:ncol(estimates)) {
                mat[jj, index] <- round(mean(abs((estimates[, 
                  jj] - theta_0)/ses[, jj]) > zval, na.rm = TRUE), 
                  digits[ii])
            }
            mat.colnames[ii] <- "Empirical power"
        }
    }
    rownames(mat) <- mat.rownames
    colnames(mat) <- mat.colnames
    return(mat)
}
<bytecode: 0x739e308>
<environment: namespace:dvmisc>
 --- function search by body ---
Function sumsim in namespace dvmisc has this body.
 ----------- END OF FAILURE REPORT -------------- 
Quitting from lines 87-95 (dvmisc.Rmd) 
Error: processing vignette 'dvmisc.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building 'dvmisc.Rmd'

SUMMARY: processing the following file failed:
  'dvmisc.Rmd'

Error: Vignette re-building failed.
Execution halted