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#' @title Regression Parameters
#' @name regr_params
#'
#' @param truth (`numeric()`)\cr
#' True (observed) values.
#' Must have the same length as `response`.
#' @param response (`numeric()`)\cr
#' Predicted response values.
#' Must have the same length as `truth`.
#' @param sample_weights (`numeric()`)\cr
#' Vector of non-negative and finite sample weights.
#' Must have the same length as `truth`.
#' The vector gets automatically normalized to sum to one.
#' Defaults to equal sample weights.
#' @param na_value (`numeric(1)`)\cr
#' Value that should be returned if the measure is not defined for the input
#' (as described in the note). Default is `NaN`.
#' @param ... (`any`)\cr
#' Additional arguments. Currently ignored.
#' @keywords internal
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#' @title Binary Classification Parameters
#' @name binary_params
#'
#' @param truth (`factor()`)\cr
#' True (observed) labels.
#' Must have the exactly same two levels and the same length as `response`.
#' @param response (`factor()`)\cr
#' Predicted response labels.
#' Must have the exactly same two levels and the same length as `truth`.
#' @param prob (`numeric()`)\cr
#' Predicted probability for positive class.
#' Must have exactly same length as `truth`.
#' @param positive (`character(1))`\cr
#' Name of the positive class.
#' @param sample_weights (`numeric()`)\cr
#' Vector of non-negative and finite sample weights.
#' Must have the same length as `truth`.
#' The vector gets automatically normalized to sum to one.
#' Defaults to equal sample weights.
#' @param na_value (`numeric(1)`)\cr
#' Value that should be returned if the measure is not defined for the input
#' (as described in the note). Default is `NaN`.
#' @param ... (`any`)\cr
#' Additional arguments. Currently ignored.
#' @keywords internal
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#' @title Classification Parameters
#' @name classif_params
#'
#' @param truth (`factor()`)\cr
#' True (observed) labels.
#' Must have the same levels and length as `response`.
#' @param response (`factor()`)\cr
#' Predicted response labels.
#' Must have the same levels and length as `truth`.
#' @param prob (`matrix()`)\cr
#' Matrix of predicted probabilities, each column is a vector of probabilities for a
#' specific class label.
#' Columns must be named with levels of `truth`.
#' @param sample_weights (`numeric()`)\cr
#' Vector of non-negative and finite sample weights.
#' Must have the same length as `truth`.
#' The vector gets automatically normalized to sum to one.
#' Defaults to equal sample weights.
#' @param na_value (`numeric(1)`)\cr
#' Value that should be returned if the measure is not defined for the input
#' (as described in the note). Default is `NaN`.
#' @param ... (`any`)\cr
#' Additional arguments. Currently ignored.
#' @keywords internal
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#' @title Similarity Parameters
#' @name similarity_params
#'
#' @param sets (`list()`)\cr
#' List of character or integer vectors.
#' `sets` must have at least 2 elements.
#' @param p (`integer(1)`)\cr
#' Total number of possible elements.
#' @param na_value (`numeric(1)`)\cr
#' Value that should be returned if the measure is not defined for the input
#' (as described in the note). Default is `NaN`.
#' @param ... (`any`)\cr
#' Additional arguments. Currently ignored.
#' @keywords internal
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