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#' Tuning Parameters for NNETAR Models
#'
#' @inheritParams dials::epochs
#'
#' @details
#' The main parameters for NNETAR models are:
#'
#' - `non_seasonal_ar`: Number of non-seasonal auto-regressive (AR) lags. Often denoted "p" in pdq-notation.
#' - `seasonal_ar`: Number of seasonal auto-regressive (SAR) lags. Often denoted "P" in PDQ-notation.
#' - `hidden_units`: An integer for the number of units in the hidden model.
#' - `num_networks`: Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.
#' - `penalty`: A non-negative numeric value for the amount of weight decay.
#' - `epochs`: An integer for the number of training iterations.
#'
#'
#' @seealso
#' [non_seasonal_ar()], [seasonal_ar()], [dials::hidden_units()], [dials::penalty()], [dials::epochs()]
#'
#' @examples
#'
#' num_networks()
#'
#' @name nnetar_params
#' @export
#' @rdname nnetar_params
num_networks <- function(range = c(1L, 100L), trans = NULL) {
dials::new_quant_param(
type = "integer",
range = range,
inclusive = c(TRUE, TRUE),
trans = trans,
label = c(num_networks = "Number of Neural Networks to Average"),
finalize = NULL
)
}
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