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#' @title Set options for Bayesian Mallows model
#'
#' @description
#' Specify various model options for the Bayesian Mallows model.
#'
#'
#' @param metric A character string specifying the distance metric to use in the
#' Bayesian Mallows Model. Available options are `"footrule"`, `"spearman"`,
#' `"cayley"`, `"hamming"`, `"kendall"`, and `"ulam"`. The distance given by
#' `metric` is also used to compute within-cluster distances, when
#' `include_wcd = TRUE`.
#'
#'
#' @param error_model Character string specifying which model to use for
#' inconsistent rankings. Defaults to `"none"`, which means that inconsistent
#' rankings are not allowed. At the moment, the only available other option is
#' `"bernoulli"`, which means that the Bernoulli error model is used. See
#' \insertCite{crispino2019;textual}{BayesMallows} for a definition of the
#' Bernoulli model.
#'
#' @param n_clusters Integer specifying the number of clusters, i.e., the number
#' of mixture components to use. Defaults to `1L`, which means no clustering
#' is performed. See [compute_mallows_mixtures()] for a convenience function
#' for computing several models with varying numbers of mixtures.
#'
#' @return An object of class `"BayesMallowsModelOptions"`, to be provided in
#' the `model_options` argument to [compute_mallows()],
#' [compute_mallows_mixtures()], or [update_mallows()].
#'
#' @export
#'
#' @family preprocessing
#'
#' @references \insertAllCited{}
#'
set_model_options <- function(
metric = c("footrule", "spearman", "cayley", "hamming", "kendall", "ulam"),
n_clusters = 1,
error_model = c("none", "bernoulli")) {
metric <- match.arg(metric, c(
"footrule", "spearman", "cayley", "hamming",
"kendall", "ulam"
))
error_model <- match.arg(error_model, c("none", "bernoulli"))
validate_integer(n_clusters)
validate_positive(n_clusters)
ret <- as.list(environment())
class(ret) <- "BayesMallowsModelOptions"
ret
}
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