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## ID: mardia.R, last updated 2020-11-22, F.Osorio
kurtosis <- function(x)
{ ## Mardia's multivariate kurtosis coefficient
if (is.data.frame(x))
x <- as.matrix(x)
else if (!is.matrix(x))
stop("'x' must be a matrix or a data frame")
if (!all(is.finite(x)))
stop("'x' must contain finite values only")
n <- nrow(x)
p <- ncol(x)
z <- cov.weighted(x, weights = rep(1, n))
storage.mode(x) <- "double"
storage.mode(z$cov) <- "double"
z <- .C("skewness_and_kurtosis",
x = x,
n = as.integer(n),
p = as.integer(p),
center = as.double(z$mean),
cov = z$cov,
stats = double(2),
task = as.integer(0))$stats[2]
attr(z, 'excess') <- z - p * (p + 2)
z
}
skewness <- function(x)
{ ## Mardia's multivariate skewness coefficient
if (is.data.frame(x))
x <- as.matrix(x)
else if (!is.matrix(x))
stop("'x' must be a matrix or a data frame")
if (!all(is.finite(x)))
stop("'x' must contain finite values only")
n <- nrow(x)
p <- ncol(x)
z <- cov.weighted(x, weights = rep(1, n))
storage.mode(x) <- "double"
storage.mode(z$cov) <- "double"
z <- .C("skewness_and_kurtosis",
x = x,
n = as.integer(n),
p = as.integer(p),
center = as.double(z$mean),
cov = z$cov,
stats = double(2),
task = as.integer(1))$stats[1]
z
}
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