# R/mlsnorm.R In univariateML: Maximum Likelihood Estimation for Univariate Densities

#### Documented in mlsnorm

#' Skew Normal distribution maximum likelihood estimation
#'
#' Joint maximum likelihood estimation as implemented by [fGarch::snormFit].
#'
#' For the density function of the Student t distribution see
#' [dsnorm][fGarch::snorm].
#'
#' @param x a (non-empty) numeric vector of data values.
#' @param na.rm logical. Should missing values be removed?
#' @param ... currently affects nothing.
#' @return mlsnorm returns an object of [class][base::class] univariateML.
#'    This is a named numeric vector with maximum likelihood estimates for
#'    the parameters mean, sd, xi and the following attributes:
#'     \item{model}{The name of the model.}
#'     \item{density}{The density associated with the estimates.}
#'     \item{logLik}{The loglikelihood at the maximum.}
#'     \item{support}{The support of the density.}
#'     \item{n}{The number of observations.}
#'     \item{call}{The call as captured my match.call}
#' @examples
#' mlsnorm(precip)
#' @seealso [dsnorm][fGarch::snorm] for the Student-t density.
#' @references Fernandez C., Steel M.F.J. (2000); On Bayesian Modelling of Fat
#'     Tails and Skewness, Preprint.
#' @export

mlsnorm <- function(x, na.rm = FALSE, ...) {
if (na.rm) x <- x[!is.na(x)] else assertthat::assert_that(!anyNA(x))
ml_input_checker(x)

fit <- suppressWarnings(fGarch::snormFit(x))
object <- fit[["par"]]
class(object) <- "univariateML"
attr(object, "model") <- "Skew Normal"
attr(object, "density") <- "fGarch::dsnorm"
attr(object, "logLik") <- -fit\$objective
attr(object, "support") <- c(-Inf, Inf)
attr(object, "n") <- length(x)
attr(object, "call") <- match.call()
object
}


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univariateML documentation built on Jan. 25, 2022, 5:09 p.m.