Running examples in 'dvmisc-Ex.R' failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: sumsim
> ### Title: Summarize Simulation Results
> ### Aliases: sumsim
> 
> ### ** Examples
> 
> # For X ~ N(mu, sigma^2), the MLE for sigma^2 is the sample variance with n 
> # in the denominator, but the unbiased version with (n - 1) is typically used 
> # for its unbiasedness. Compare these estimators in 1,000 trials with n = 25. 
> MLE <- c()
> Unbiased <- c()
> for (ii in 1: 1000) {
+    x <- rnorm(n = 25)
+    MLE[ii] <- sum((x - mean(x))^2) / 25
+    Unbiased[ii] <- sum((x - mean(x))^2) / 24
+  }
> sumsim(estimates = cbind(MLE, Unbiased), truth = 1)
 ----------- FAILURE REPORT -------------- 
 --- failure: the condition has length > 1 ---
 --- srcref --- 
: 
 --- package (from environment) --- 
dvmisc
 --- call from context --- 
sumsim(estimates = cbind(MLE, Unbiased), truth = 1)
 --- call from argument --- 
if (class(estimates) != "matrix") {
    estimates <- as.matrix(estimates)
}
 --- R stacktrace ---
where 1: sumsim(estimates = cbind(MLE, Unbiased), truth = 1)

 --- 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: 0xd065480>
<environment: namespace:dvmisc>
 --- function search by body ---
Function sumsim in namespace dvmisc has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: the condition has length > 1