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#' Tuning Parameters for ADAM Models
#'
#' @param values A character string of possible values.
#'
#' @details
#' The main parameters for ADAM models are:
#'
#' - `non_seasonal_ar`: The order of the non-seasonal auto-regressive (AR) terms.
#' - `non_seasonal_differences`: The order of integration for non-seasonal differencing.
#' - `non_seasonal_ma`: The order of the non-seasonal moving average (MA) terms.
#' - `seasonal_ar`: The order of the seasonal auto-regressive (SAR) terms.
#' - `seasonal_differences`: The order of integration for seasonal differencing.
#' - `seasonal_ma`: The order of the seasonal moving average (SMA) terms.
#' - `use_constant`: Logical, determining, whether the constant is needed in the model or not.
#' - `regressors_treatment`: The variable defines what to do with the provided explanatory variables.
#' - `outliers_treatment`: Defines what to do with outliers.
#' - `probability_model`: The type of model used in probability estimation.
#' - `distribution`: What density function to assume for the error term.
#' - `information_criteria`: The information criterion to use in the model selection / combination procedure.
#' - `select_order`: If TRUE, then the function will select the most appropriate order.
#'
#' @returns A `dials` parameter
#'
#' @examples
#' use_constant()
#'
#' regressors_treatment()
#'
#' distribution()
#'
#'
#' @name adam_params
#' @export
#' @return A parameter
#' @rdname adam_params
use_constant <- function(values = c(FALSE, TRUE)) {
dials::new_qual_param(
type = "logical",
default = FALSE,
values = values,
label = c(use_constant = "Logical, determining, whether the constant is needed in the model or not"),
finalize = NULL
)
}
#' @export
#' @return A parameter
#' @rdname adam_params
regressors_treatment <- function(values = c("use", "select", "adapt")) {
dials::new_qual_param(
type = "character",
default = "use",
values = values,
label = c(regressors_treatment = "The variable defines what to do with the provided explanatory variables."),
finalize = NULL
)
}
#' @export
#' @return A parameter
#' @rdname adam_params
outliers_treatment <- function(values = c( "ignore", "use", "select")) {
dials::new_qual_param(
type = "character",
values = values,
default = "ignore",
label = c(outliers_treatment = "Defines what to do with outliers."),
finalize = NULL
)
}
#' @export
#' @return A parameter
#' @rdname adam_params
probability_model <- function(values= c("none", "auto", "fixed", "general", "odds-ratio", "inverse-odds-ratio", "direct")) {
dials::new_qual_param(
type = "character",
default = "none",
values = values,
label = c(probability_model = "The type of model used in probability estimation."),
finalize = NULL
)
}
#' @export
#' @return A parameter
#' @rdname adam_params
distribution <- function(values = c("default", "dnorm", "dlaplace", "ds", "dgnorm", "dlnorm", "dinvgauss", "dgamma")) {
dials::new_qual_param(
type = "character",
default = "default",
values = values,
label = c(distribution = "What density function to assume for the error term."),
finalize = NULL
)
}
#' @export
#' @return A parameter
#' @rdname adam_params
information_criteria <- function(values = c("AICc", "AIC", "BICc", "BIC")) {
dials::new_qual_param(
type = "character",
values = values,
default = "AICc",
label = c(information_criteria = "The information criterion to use in the model selection / combination procedure."),
finalize = NULL
)
}
#' @export
#' @return A parameter
#' @rdname adam_params
select_order <- function(values = c(FALSE, TRUE)) {
dials::new_qual_param(
type = "logical",
default = FALSE,
values = values,
label = c(select_order = "If TRUE, then the function will select the most appropriate order."),
finalize = NULL
)
}
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