``````#' Monte-Carlo Standard Error (MCSE)
#'
#' This function returns the Monte Carlo Standard Error (MCSE).
#'
#' @inheritParams effective_sample
#'
#'
#' @details \strong{Monte Carlo Standard Error (MCSE)} is another measure of
#' accuracy of the chains. It is defined as standard deviation of the chains
#' divided by their effective sample size (the formula for \code{mcse()} is
#' from Kruschke 2015, p. 187). The MCSE \dQuote{provides a quantitative
#' suggestion of how big the estimation noise is}.
#'
#' @references Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
#'
#' @examples
#' library(bayestestR)
#' library(rstanarm)
#'
#' model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
#' mcse(model)
#' @importFrom insight get_parameters
#' @export
mcse <- function(model, ...) {
UseMethod("mcse")
}

#' @export
mcse.brmsfit <- function(model, effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, ...) {
# check arguments
effects <- match.arg(effects)
component <- match.arg(component)

params <-
insight::get_parameters(
model,
effects = effects,
component = component,
parameters = parameters
)

ess <-
effective_sample(
model,
effects = effects,
component = component,
parameters = parameters
)

.mcse(params, ess\$ESS)
}

#' @rdname mcse
#' @export
mcse.stanreg <- function(model, effects = c("fixed", "random", "all"), parameters = NULL, ...) {
# check arguments
effects <- match.arg(effects)

params <-
insight::get_parameters(
model,
effects = effects,
parameters = parameters
)

ess <-
effective_sample(
model,
effects = effects,
parameters = parameters
)

.mcse(params, ess\$ESS)
}

#' @importFrom stats sd
#' @keywords internal
.mcse <- function(params, ess) {
# get standard deviations from posterior samples
stddev <- sapply(params, stats::sd)

# compute mcse
data.frame(
Parameter = colnames(params),
MCSE = stddev / sqrt(ess),
stringsAsFactors = FALSE,
row.names = NULL
)
}
``````