Nothing
dlnormTruncAlt <-
function (x, mean = exp(1/2), cv = sqrt(exp(1) - 1), min = 0,
max = Inf)
{
names.x <- names(x)
arg.mat <- cbind.no.warn(x = as.vector(x), mean = as.vector(mean),
cv = as.vector(cv), min = as.vector(min), max = as.vector(max))
na.index <- is_na_matrix(arg.mat)
if (all(na.index))
y <- rep(NA, nrow(arg.mat))
else {
y <- numeric(nrow(arg.mat))
y[na.index] <- NA
y.no.na <- y[!na.index]
for (i in c("x", "mean", "cv", "min", "max")) assign(i,
arg.mat[!na.index, i])
if (any(mean < .Machine$double.eps))
stop("All non-missing values of 'mean' must be positive.")
if (any(cv < .Machine$double.eps))
stop("All non-missing values of 'cv' must be positive.")
if (any(min >= max))
stop(paste("All non-missing values of 'min' must be",
"less than the corresponding elements of 'max'."))
if (any(min < 0))
stop(paste("All non-missing values of 'min' must be",
"greater than or equal to 0."))
x.out <- x < min | x > max
y.no.na[x.out] <- 0
if (any(index <- !x.out)) {
mean <- mean[index]
cv <- cv[index]
y.no.na[index] <- dlnormAlt(x[index], mean = mean,
cv = cv)/(plnormAlt(max[index], mean = mean,
cv = cv) - plnormAlt(min[index], mean = mean,
cv = cv))
}
y[!na.index] <- y.no.na
}
if (!is.null(names.x))
names(y) <- rep(names.x, length = length(y))
y
}
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