Error(s) in re-building vignettes:
  ...
--- re-building 'HDMT.Rnw' using Sweave
 ----------- FAILURE REPORT -------------- 
 --- failure: the condition has length > 1 ---
 --- srcref --- 
: 
 --- package (from environment) --- 
HDMT
 --- call from context --- 
nullestimation(input_pvalues, lambda = 0.5)
 --- call from argument --- 
if (!class(input_pvalues) %in% c("matrix", "data.frame")) stop("input_pvalues should be a matrix or data frame")
 --- R stacktrace ---
where 1: nullestimation(input_pvalues, lambda = 0.5)
where 2: eval(expr, .GlobalEnv)
where 3: eval(expr, .GlobalEnv)
where 4: withVisible(eval(expr, .GlobalEnv))
where 5: doTryCatch(return(expr), name, parentenv, handler)
where 6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 7: tryCatchList(expr, classes, parentenv, handlers)
where 8: 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)[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))
})
where 9: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
where 10: evalFunc(ce, options)
where 11: tryCatchList(expr, classes, parentenv, handlers)
where 12: tryCatch(evalFunc(ce, options), finally = {
    cat("\n")
    sink()
})
where 13: driver$runcode(drobj, chunk, chunkopts)
where 14: utils::Sweave(...)
where 15: engine$weave(file, quiet = quiet, encoding = enc)
where 16: doTryCatch(return(expr), name, parentenv, handler)
where 17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 18: tryCatchList(expr, classes, parentenv, handlers)
where 19: 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 20: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/HDMT.Rcheck/vign_test/HDMT", 
    ser_elibs = "/tmp/RtmpzBlHmE/file63715c438e43.rds")

 --- value of length: 2 type: logical ---
[1] FALSE  TRUE
 --- function from context --- 
function (input_pvalues, lambda = 0.5) 
{
    if (!class(input_pvalues) %in% c("matrix", "data.frame")) 
        stop("input_pvalues should be a matrix or data frame")
    if (ncol(input_pvalues) != 2) 
        stop("inpute_pvalues should have 2 column")
    input_pvalues <- matrix(as.numeric(input_pvalues), nrow = nrow(input_pvalues))
    if (sum(complete.cases(input_pvalues)) < nrow(input_pvalues)) 
        warning("input_pvalues contains NAs to be removed from analysis")
    input_pvalues <- input_pvalues[complete.cases(input_pvalues), 
        ]
    if (!is.null(nrow(input_pvalues)) & nrow(input_pvalues) < 
        1) 
        stop("input_pvalues doesn't have valid p-values")
    pcut <- seq(0.1, 0.8, 0.1)
    frac1 <- rep(0, 8)
    frac2 <- rep(0, 8)
    frac12 <- rep(0, 8)
    for (i in 1:8) {
        frac1[i] <- mean(input_pvalues[, 1] >= pcut[i])/(1 - 
            pcut[i])
        frac2[i] <- mean(input_pvalues[, 2] >= pcut[i])/(1 - 
            pcut[i])
        frac12[i] <- mean(input_pvalues[, 2] >= pcut[i] & input_pvalues[, 
            1] >= pcut[i])/(1 - pcut[i])^2
    }
    alpha00 <- min(frac12[pcut == lambda], 1)
    if (ks.test(input_pvalues[, 1], "punif", 0, 1)$p > 0.05) 
        alpha1 <- 1
    else alpha1 <- estim.pi0(p = input_pvalues[, 1], pi0.method = "slim")$pi0.SLIM
    if (ks.test(input_pvalues[, 2], "punif", 0, 1)$p > 0.05) 
        alpha2 <- 1
    else alpha2 <- estim.pi0(p = input_pvalues[, 2], pi0.method = "slim")$pi0.SLIM
    if (alpha1 == 1 | alpha2 == 1) {
        alpha11 <- 0
        alpha10 <- 1 - alpha1
        alpha01 <- 1 - alpha2
        if (alpha1 == 1 & alpha2 == 1) 
            alpha00 <- 1
        else {
            if (sum(alpha00 + alpha10 + alpha01) > 1) {
                err <- sum(alpha00 + alpha10 + alpha01) - 1
                alpha00 <- alpha00 - err
            }
            else {
                err <- 1 - sum(alpha00 + alpha10 + alpha01)
                if (alpha10 != 0) 
                  alpha10 <- alpha10 + err
                if (alpha01 != 0) 
                  alpha01 <- alpha01 + err
            }
        }
    }
    else {
        alpha11 <- max(1 - alpha1 + 1 - alpha2 + alpha00 - 1, 
            0)
        if (alpha11 == 0) {
            alpha10 <- 1 - alpha1
            alpha01 <- 1 - alpha2
            err <- 1 - (alpha00 + alpha10 + alpha01)
            alpha00 <- alpha00 + err
        }
        else {
            if (alpha11 > (1 - alpha1) | alpha11 > (1 - alpha2)) {
                alpha11 <- 0
                alpha10 <- 1 - alpha1
                alpha01 <- 1 - alpha2
                err <- 1 - (alpha00 + alpha10 + alpha01)
                alpha00 <- alpha00 + err
            }
            else {
                alpha10 <- max(1 - alpha1 - alpha11, 0)
                alpha01 <- max(1 - alpha2 - alpha11, 0)
                alpha00 <- 1 - alpha10 - alpha01 - alpha11
            }
        }
    }
    alpha.null <- list(alpha10 = alpha10, alpha01 = alpha01, 
        alpha00 = alpha00, alpha1 = alpha1, alpha2 = alpha2)
    return(alpha.null)
}
<bytecode: 0x20f0498>
<environment: namespace:HDMT>
 --- function search by body ---
Function nullestimation in namespace HDMT has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: the condition has length > 1