r check updates

DESCRIPTION

@@ -50,6 +50,7 @@ Imports:
     plyr,
     purrr,
     recipes,
+    rlang,
     rsample,
     rules,
     snakecase,

NAMESPACE

@@ -78,4 +78,5 @@ importFrom(rules,cubist_fit)
 importFrom(rules,max_rules)
 importFrom(stats,frequency)
 importFrom(stats,sd)
+importFrom(stats,setNames)
 importFrom(stats,update)

R/multistep_cubist.R

@@ -132,7 +132,7 @@ make_cubist_multistep <- function() {
 #' @param selected_features selected features
 #'
 #' @return Get Multistep Horizon CUBIST model
-#' @noRd
+#' @keywords internal
 #' @export
 cubist_multistep <- function(mode = "regression",
                              committees = NULL, neighbors = NULL,
@@ -166,7 +166,7 @@ cubist_multistep <- function(mode = "regression",
 #'
 #'
 #' @return Prints model info
-#' @noRd
+#' @keywords internal
 #' @export
 print.cubist_multistep <- function(x, ...) {
   cat("CUBIST Multistep Horizon (", x$mode, ")\n\n", sep = "")
@@ -195,7 +195,7 @@ print.cubist_multistep <- function(x, ...) {
 #' @param ... extra args passed to cubist
 #'
 #' @return Updated model
-#' @noRd
+#' @keywords internal
 #' @importFrom stats update
 #' @export
 update.cubist_multistep <- function(object,
@@ -258,7 +258,7 @@ update.cubist_multistep <- function(object,
 #'
 #'
 #' @return translated model
-#' @noRd
+#' @keywords internal
 #' @importFrom parsnip translate
 #' @export
 translate.cubist_multistep <- function(x, engine = x$engine, ...) {
@@ -285,7 +285,7 @@ translate.cubist_multistep <- function(x, engine = x$engine, ...) {
 #' @param forecast_horizon forecast horizon
 #' @param selected_features selected features
 #'
-#' @noRd
+#' @keywords internal
 #' @importFrom stats frequency
 #' @export
 cubist_multistep_fit_impl <- function(x, y,
@@ -399,7 +399,7 @@ cubist_multistep_fit_impl <- function(x, y,
 #'
 #'
 #' @return prints custom model
-#' @noRd
+#' @keywords internal
 #' @export
 print.cubist_multistep_fit_impl <- function(x, ...) {
   if (!is.null(x$desc)) cat(paste0(x$desc, "\n"))
@@ -422,7 +422,7 @@ print.cubist_multistep_fit_impl <- function(x, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 predict.cubist_multistep_fit_impl <- function(object, new_data, ...) {
   cubist_multistep_predict_impl(object, new_data, ...)
@@ -435,7 +435,7 @@ predict.cubist_multistep_fit_impl <- function(object, new_data, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 cubist_multistep_predict_impl <- function(object, new_data, ...) {
 

R/multistep_glmnet.R

@@ -123,7 +123,7 @@ make_glmnet_multistep <- function() {
 #' @param selected_features selected features
 #'
 #' @return Get Multistep Horizon GLMNET model
-#' @noRd
+#' @keywords internal
 #' @export
 glmnet_multistep <- function(mode = "regression",
                              mixture = NULL, penalty = NULL,
@@ -156,7 +156,7 @@ glmnet_multistep <- function(mode = "regression",
 #'
 #'
 #' @return Prints model info
-#' @noRd
+#' @keywords internal
 #' @export
 print.glmnet_multistep <- function(x, ...) {
   cat("GLMNET Multistep Horizon (", x$mode, ")\n\n", sep = "")
@@ -184,7 +184,7 @@ print.glmnet_multistep <- function(x, ...) {
 #' @param ... extra args passed to glmnet
 #'
 #' @return Updated model
-#' @noRd
+#' @keywords internal
 #' @importFrom stats update
 #' @export
 update.glmnet_multistep <- function(object,
@@ -244,7 +244,7 @@ update.glmnet_multistep <- function(object,
 #'
 #'
 #' @return translated model
-#' @noRd
+#' @keywords internal
 #' @importFrom parsnip translate
 #' @export
 translate.glmnet_multistep <- function(x, engine = x$engine, ...) {
@@ -270,7 +270,7 @@ translate.glmnet_multistep <- function(x, engine = x$engine, ...) {
 #' @param forecast_horizon forecast horizon
 #' @param selected_features selected features
 #'
-#' @noRd
+#' @keywords internal
 #' @importFrom stats frequency
 #' @export
 glmnet_multistep_fit_impl <- function(x, y,
@@ -387,7 +387,7 @@ glmnet_multistep_fit_impl <- function(x, y,
 #'
 #'
 #' @return prints custom model
-#' @noRd
+#' @keywords internal
 #' @export
 print.glmnet_multistep_fit_impl <- function(x, ...) {
   if (!is.null(x$desc)) cat(paste0(x$desc, "\n"))
@@ -410,7 +410,7 @@ print.glmnet_multistep_fit_impl <- function(x, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 predict.glmnet_multistep_fit_impl <- function(object, new_data, ...) {
   glmnet_multistep_predict_impl(object, new_data, ...)
@@ -423,7 +423,7 @@ predict.glmnet_multistep_fit_impl <- function(object, new_data, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 glmnet_multistep_predict_impl <- function(object, new_data, ...) {
 

R/multistep_mars.R

@@ -133,7 +133,7 @@ make_mars_multistep <- function() {
 #' @param selected_features selected features
 #'
 #' @return Get Multistep Horizon MARS model
-#' @noRd
+#' @keywords internal
 #' @export
 mars_multistep <- function(mode = "regression",
                            num_terms = NULL,
@@ -169,7 +169,7 @@ mars_multistep <- function(mode = "regression",
 #'
 #'
 #' @return Prints model info
-#' @noRd
+#' @keywords internal
 #' @export
 print.mars_multistep <- function(x, ...) {
   cat("MARS Multistep Horizon (", x$mode, ")\n\n", sep = "")
@@ -199,7 +199,7 @@ print.mars_multistep <- function(x, ...) {
 #' @param ... extra args passed to mars
 #'
 #' @return Updated model
-#' @noRd
+#' @keywords internal
 #' @importFrom stats update
 #' @export
 update.mars_multistep <- function(object,
@@ -262,7 +262,7 @@ update.mars_multistep <- function(object,
 #'
 #'
 #' @return translated model
-#' @noRd
+#' @keywords internal
 #' @importFrom parsnip translate
 #' @export
 translate.mars_multistep <- function(x, engine = x$engine, ...) {
@@ -290,7 +290,7 @@ translate.mars_multistep <- function(x, engine = x$engine, ...) {
 #' @param forecast_horizon forecast horizon
 #' @param selected_features selected features
 #'
-#' @noRd
+#' @keywords internal
 #' @importFrom stats frequency
 #' @export
 mars_multistep_fit_impl <- function(x, y,
@@ -410,7 +410,7 @@ mars_multistep_fit_impl <- function(x, y,
 #'
 #'
 #' @return prints custom model
-#' @noRd
+#' @keywords internal
 #' @export
 print.mars_multistep_fit_impl <- function(x, ...) {
   if (!is.null(x$desc)) cat(paste0(x$desc, "\n"))
@@ -433,7 +433,7 @@ print.mars_multistep_fit_impl <- function(x, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 predict.mars_multistep_fit_impl <- function(object, new_data, ...) {
   mars_multistep_predict_impl(object, new_data, ...)
@@ -446,7 +446,7 @@ predict.mars_multistep_fit_impl <- function(object, new_data, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 mars_multistep_predict_impl <- function(object, new_data, ...) {
 

R/multistep_svm_poly.R

@@ -144,7 +144,7 @@ make_svm_poly_multistep <- function() {
 #' @param selected_features selected features
 #'
 #' @return Get Multistep Horizon SVM-POLY model
-#' @noRd
+#' @keywords internal
 #' @export
 svm_poly_multistep <- function(mode = "regression",
                                cost = NULL,
@@ -182,7 +182,7 @@ svm_poly_multistep <- function(mode = "regression",
 #'
 #'
 #' @return Prints model info
-#' @noRd
+#' @keywords internal
 #' @export
 print.svm_poly_multistep <- function(x, ...) {
   cat("SVM-POLY Multistep Horizon (", x$mode, ")\n\n", sep = "")
@@ -214,7 +214,7 @@ print.svm_poly_multistep <- function(x, ...) {
 #' @param ... extra args passed to svm_poly
 #'
 #' @return Updated model
-#' @noRd
+#' @keywords internal
 #' @importFrom stats update
 #' @export
 update.svm_poly_multistep <- function(object,
@@ -278,7 +278,7 @@ update.svm_poly_multistep <- function(object,
 #'
 #'
 #' @return translated model
-#' @noRd
+#' @keywords internal
 #' @importFrom parsnip translate
 #' @export
 translate.svm_poly_multistep <- function(x, engine = x$engine, ...) {
@@ -309,7 +309,7 @@ translate.svm_poly_multistep <- function(x, engine = x$engine, ...) {
 #' @param forecast_horizon forecast horizon
 #' @param selected_features selected features
 #'
-#' @noRd
+#' @keywords internal
 #' @importFrom stats frequency
 #' @export
 svm_poly_multistep_fit_impl <- function(x, y,
@@ -434,7 +434,7 @@ svm_poly_multistep_fit_impl <- function(x, y,
 #'
 #'
 #' @return prints custom model
-#' @noRd
+#' @keywords internal
 #' @export
 print.svm_poly_multistep_fit_impl <- function(x, ...) {
   if (!is.null(x$desc)) cat(paste0(x$desc, "\n"))
@@ -457,7 +457,7 @@ print.svm_poly_multistep_fit_impl <- function(x, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 predict.svm_poly_multistep_fit_impl <- function(object, new_data, ...) {
   svm_poly_multistep_predict_impl(object, new_data, ...)
@@ -470,7 +470,7 @@ predict.svm_poly_multistep_fit_impl <- function(object, new_data, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 svm_poly_multistep_predict_impl <- function(object, new_data, ...) {
 

R/multistep_svm_rbf.R

@@ -134,7 +134,7 @@ make_svm_rbf_multistep <- function() {
 #' @param selected_features selected features
 #'
 #' @return Get Multistep Horizon SVM-RBF model
-#' @noRd
+#' @keywords internal
 #' @export
 svm_rbf_multistep <- function(mode = "regression",
                               cost = NULL,
@@ -170,7 +170,7 @@ svm_rbf_multistep <- function(mode = "regression",
 #'
 #'
 #' @return Prints model info
-#' @noRd
+#' @keywords internal
 #' @export
 print.svm_rbf_multistep <- function(x, ...) {
   cat("SVM-RBF Multistep Horizon (", x$mode, ")\n\n", sep = "")
@@ -200,7 +200,7 @@ print.svm_rbf_multistep <- function(x, ...) {
 #' @param ... extra args passed to svm_rbf
 #'
 #' @return Updated model
-#' @noRd
+#' @keywords internal
 #' @importFrom stats update
 #' @export
 update.svm_rbf_multistep <- function(object,
@@ -262,7 +262,7 @@ update.svm_rbf_multistep <- function(object,
 #'
 #'
 #' @return translated model
-#' @noRd
+#' @keywords internal
 #' @importFrom parsnip translate
 #' @export
 translate.svm_rbf_multistep <- function(x, engine = x$engine, ...) {
@@ -291,7 +291,7 @@ translate.svm_rbf_multistep <- function(x, engine = x$engine, ...) {
 #' @param forecast_horizon forecast horizon
 #' @param selected_features selected features
 #'
-#' @noRd
+#' @keywords internal
 #' @importFrom stats frequency
 #' @export
 svm_rbf_multistep_fit_impl <- function(x, y,
@@ -414,7 +414,7 @@ svm_rbf_multistep_fit_impl <- function(x, y,
 #'
 #'
 #' @return prints custom model
-#' @noRd
+#' @keywords internal
 #' @export
 print.svm_rbf_multistep_fit_impl <- function(x, ...) {
   if (!is.null(x$desc)) cat(paste0(x$desc, "\n"))
@@ -437,7 +437,7 @@ print.svm_rbf_multistep_fit_impl <- function(x, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 predict.svm_rbf_multistep_fit_impl <- function(object, new_data, ...) {
   svm_rbf_multistep_predict_impl(object, new_data, ...)
@@ -450,7 +450,7 @@ predict.svm_rbf_multistep_fit_impl <- function(object, new_data, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 svm_rbf_multistep_predict_impl <- function(object, new_data, ...) {
 

R/multistep_xgboost.R

@@ -179,7 +179,7 @@ make_xgboost_multistep <- function() {
 #' @param selected_features selected features
 #'
 #' @return Get Multistep Horizon XGBoost model
-#' @noRd
+#' @keywords internal
 #' @export
 xgboost_multistep <- function(mode = "regression",
                               mtry = NULL, trees = NULL, min_n = NULL,
@@ -219,7 +219,7 @@ xgboost_multistep <- function(mode = "regression",
 #'
 #'
 #' @return Prints model info
-#' @noRd
+#' @keywords internal
 #' @export
 print.xgboost_multistep <- function(x, ...) {
   cat("XGBoost Multistep Horizon (", x$mode, ")\n\n", sep = "")
@@ -253,7 +253,7 @@ print.xgboost_multistep <- function(x, ...) {
 #' @param ... extra args passed to xgboost
 #'
 #' @return Updated model
-#' @noRd
+#' @keywords internal
 #' @importFrom stats update
 #' @export
 update.xgboost_multistep <- function(object,
@@ -323,7 +323,7 @@ update.xgboost_multistep <- function(object,
 #'
 #'
 #' @return translated model
-#' @noRd
+#' @keywords internal
 #' @importFrom parsnip translate
 #' @export
 translate.xgboost_multistep <- function(x, engine = x$engine, ...) {
@@ -368,7 +368,7 @@ translate.xgboost_multistep <- function(x, engine = x$engine, ...) {
 #' @param ... Additional arguments passed to `xgboost::xgb.train`
 #'
 #'
-#' @noRd
+#' @keywords internal
 #' @importFrom stats frequency
 #' @export
 xgboost_multistep_fit_impl <- function(x, y,
@@ -497,7 +497,7 @@ xgboost_multistep_fit_impl <- function(x, y,
 #'
 #'
 #' @return prints custom model
-#' @noRd
+#' @keywords internal
 #' @export
 print.xgboost_multistep_fit_impl <- function(x, ...) {
   if (!is.null(x$desc)) cat(paste0(x$desc, "\n"))
@@ -520,7 +520,7 @@ print.xgboost_multistep_fit_impl <- function(x, ...) {
 #' @param new_data input data to predict
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 predict.xgboost_multistep_fit_impl <- function(object, new_data, ...) {
   xgboost_multistep_predict_impl(object, new_data, ...)
@@ -534,7 +534,7 @@ predict.xgboost_multistep_fit_impl <- function(object, new_data, ...) {
 #' @param ... Additional arguments passed to `predict.xgb.Booster()`
 #'
 #' @return predictions
-#' @noRd
+#' @keywords internal
 #' @export
 xgboost_multistep_predict_impl <- function(object, new_data, ...) {
 

R/utility.R

@@ -14,14 +14,15 @@ utils::globalVariables(c(
   ".config", "Forecast_Tbl", "Model_Workflow", "id", "model_run",
   "Auto_Accept", "Feature", "Imp", "Importance", "LOFO_Var", "Var_RMSE", "Vote", "Votes", "desc",
   "term", "Column", "Box_Cox_Lambda", "get_recipie_configurable", "Agg", "Unique", "Var",
-  "Var_Combo", "regressor", "regressor_tbl", "value_level_iter"
+  "Var_Combo", "regressor", "regressor_tbl", "value_level_iter", ".actual", ".fitted",
+  "forecast_horizon", "lag", "new_data", "object", "fit"
 ))
 
 #' @importFrom magrittr %>%
 
 #' @importFrom methods formalArgs
 
-#' @importFrom stats sd
+#' @importFrom stats sd setNames
 
 #' @importFrom foreach %do% %dopar%
 

man/cubist_multistep.Rd

@@ -0,0 +1,40 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{cubist_multistep}
+\alias{cubist_multistep}
+\title{CUBIST Multistep Horizon}
+\usage{
+cubist_multistep(
+  mode = "regression",
+  committees = NULL,
+  neighbors = NULL,
+  max_rules = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{mode}{A single character string for the type of model.
+The only possible value for this model is "regression".}
+
+\item{committees}{committees}
+
+\item{neighbors}{neighbors}
+
+\item{max_rules}{max rules}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\value{
+Get Multistep Horizon CUBIST model
+}
+\description{
+CUBIST Multistep Horizon
+}
+\keyword{internal}

man/cubist_multistep_fit_impl.Rd

@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{cubist_multistep_fit_impl}
+\alias{cubist_multistep_fit_impl}
+\title{Bridge CUBIST Multistep Modeling function}
+\usage{
+cubist_multistep_fit_impl(
+  x,
+  y,
+  committees = 1,
+  neighbors = 0,
+  max_rules = 10,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{x}{A dataframe of xreg (exogenous regressors)}
+
+\item{y}{A numeric vector of values to fit}
+
+\item{committees}{committees}
+
+\item{neighbors}{neighbors}
+
+\item{max_rules}{max rules}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\description{
+Bridge CUBIST Multistep Modeling function
+}
+\keyword{internal}

man/cubist_multistep_predict_impl.Rd

@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{cubist_multistep_predict_impl}
+\alias{cubist_multistep_predict_impl}
+\title{Bridge prediction Function for CUBIST Multistep Horizon Models}
+\usage{
+cubist_multistep_predict_impl(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+
+\item{...}{Additional \code{parsnip}-related options, depending on the
+value of \code{type}. Arguments to the underlying model's prediction
+function cannot be passed here (use the \code{opts} argument instead).
+Possible arguments are:
+\itemize{
+\item \code{interval}: for \code{type} equal to \code{"survival"} or \code{"quantile"}, should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type} equal to \code{"conf_int"}, \code{"pred_int"}, or \code{"survival"},
+this is the parameter for the tail area of the intervals
+(e.g. confidence level for confidence intervals).
+Default value is \code{0.95}.
+\item \code{std_error}: for \code{type} equal to \code{"conf_int"} or \code{"pred_int"}, add
+the standard error of fit or prediction (on the scale of the
+linear predictors). Default value is \code{FALSE}.
+\item \code{quantile}: for \code{type} equal to \code{quantile}, the quantiles of the
+distribution. Default is \code{(1:9)/10}.
+\item \code{eval_time}: for \code{type} equal to \code{"survival"} or \code{"hazard"}, the
+time points at which the survival probability or hazard is estimated.
+}}
+}
+\value{
+predictions
+}
+\description{
+Bridge prediction Function for CUBIST Multistep Horizon Models
+}
+\keyword{internal}

man/glmnet_multistep.Rd

@@ -0,0 +1,39 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{glmnet_multistep}
+\alias{glmnet_multistep}
+\title{GLMNET Multistep Horizon}
+\usage{
+glmnet_multistep(
+  mode = "regression",
+  mixture = NULL,
+  penalty = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{mode}{A single character string for the type of model.
+The only possible value for this model is "regression".}
+
+\item{mixture}{mixture}
+
+\item{penalty}{penalty}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\value{
+Get Multistep Horizon GLMNET model
+}
+\description{
+GLMNET Multistep Horizon
+}
+\keyword{internal}

man/glmnet_multistep_fit_impl.Rd

@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{glmnet_multistep_fit_impl}
+\alias{glmnet_multistep_fit_impl}
+\title{Bridge GLMNET Multistep Modeling function}
+\usage{
+glmnet_multistep_fit_impl(
+  x,
+  y,
+  alpha = 0,
+  lambda = 1,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{x}{A dataframe of xreg (exogenous regressors)}
+
+\item{y}{A numeric vector of values to fit}
+
+\item{alpha}{alpha}
+
+\item{lambda}{lambda}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\description{
+Bridge GLMNET Multistep Modeling function
+}
+\keyword{internal}

man/glmnet_multistep_predict_impl.Rd

@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{glmnet_multistep_predict_impl}
+\alias{glmnet_multistep_predict_impl}
+\title{Bridge prediction Function for GLMNET Multistep Horizon Models}
+\usage{
+glmnet_multistep_predict_impl(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+
+\item{...}{Additional \code{parsnip}-related options, depending on the
+value of \code{type}. Arguments to the underlying model's prediction
+function cannot be passed here (use the \code{opts} argument instead).
+Possible arguments are:
+\itemize{
+\item \code{interval}: for \code{type} equal to \code{"survival"} or \code{"quantile"}, should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type} equal to \code{"conf_int"}, \code{"pred_int"}, or \code{"survival"},
+this is the parameter for the tail area of the intervals
+(e.g. confidence level for confidence intervals).
+Default value is \code{0.95}.
+\item \code{std_error}: for \code{type} equal to \code{"conf_int"} or \code{"pred_int"}, add
+the standard error of fit or prediction (on the scale of the
+linear predictors). Default value is \code{FALSE}.
+\item \code{quantile}: for \code{type} equal to \code{quantile}, the quantiles of the
+distribution. Default is \code{(1:9)/10}.
+\item \code{eval_time}: for \code{type} equal to \code{"survival"} or \code{"hazard"}, the
+time points at which the survival probability or hazard is estimated.
+}}
+}
+\value{
+predictions
+}
+\description{
+Bridge prediction Function for GLMNET Multistep Horizon Models
+}
+\keyword{internal}

man/mars_multistep.Rd

@@ -0,0 +1,43 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{mars_multistep}
+\alias{mars_multistep}
+\title{MARS Multistep Horizon}
+\usage{
+mars_multistep(
+  mode = "regression",
+  num_terms = NULL,
+  prod_degree = NULL,
+  prune_method = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{mode}{A single character string for the type of model.
+The only possible value for this model is "regression".}
+
+\item{num_terms}{The number of features that will be retained in
+the final model, including the intercept.}
+
+\item{prod_degree}{The highest possible interaction degree.}
+
+\item{prune_method}{The pruning method.}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\value{
+Get Multistep Horizon MARS model
+}
+\description{
+MARS Multistep Horizon
+}
+\keyword{internal}

man/mars_multistep_fit_impl.Rd

@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{mars_multistep_fit_impl}
+\alias{mars_multistep_fit_impl}
+\title{Bridge MARS Multistep Modeling function}
+\usage{
+mars_multistep_fit_impl(
+  x,
+  y,
+  nprune = NULL,
+  degree = 1L,
+  pmethod = "backward",
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{x}{A dataframe of xreg (exogenous regressors)}
+
+\item{y}{A numeric vector of values to fit}
+
+\item{nprune}{The number of features that will be retained in
+the final model, including the intercept.}
+
+\item{degree}{The highest possible interaction degree.}
+
+\item{pmethod}{The pruning method.}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\description{
+Bridge MARS Multistep Modeling function
+}
+\keyword{internal}

man/mars_multistep_predict_impl.Rd

@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{mars_multistep_predict_impl}
+\alias{mars_multistep_predict_impl}
+\title{Bridge prediction Function for mars Multistep Horizon Models}
+\usage{
+mars_multistep_predict_impl(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+
+\item{...}{Additional \code{parsnip}-related options, depending on the
+value of \code{type}. Arguments to the underlying model's prediction
+function cannot be passed here (use the \code{opts} argument instead).
+Possible arguments are:
+\itemize{
+\item \code{interval}: for \code{type} equal to \code{"survival"} or \code{"quantile"}, should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type} equal to \code{"conf_int"}, \code{"pred_int"}, or \code{"survival"},
+this is the parameter for the tail area of the intervals
+(e.g. confidence level for confidence intervals).
+Default value is \code{0.95}.
+\item \code{std_error}: for \code{type} equal to \code{"conf_int"} or \code{"pred_int"}, add
+the standard error of fit or prediction (on the scale of the
+linear predictors). Default value is \code{FALSE}.
+\item \code{quantile}: for \code{type} equal to \code{quantile}, the quantiles of the
+distribution. Default is \code{(1:9)/10}.
+\item \code{eval_time}: for \code{type} equal to \code{"survival"} or \code{"hazard"}, the
+time points at which the survival probability or hazard is estimated.
+}}
+}
+\value{
+predictions
+}
+\description{
+Bridge prediction Function for mars Multistep Horizon Models
+}
+\keyword{internal}

man/predict.cubist_multistep_fit_impl.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{predict.cubist_multistep_fit_impl}
+\alias{predict.cubist_multistep_fit_impl}
+\title{Predict custom cubist model}
+\usage{
+\method{predict}{cubist_multistep_fit_impl}(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+}
+\value{
+predictions
+}
+\description{
+Predict custom cubist model
+}
+\keyword{internal}

man/predict.glmnet_multistep_fit_impl.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{predict.glmnet_multistep_fit_impl}
+\alias{predict.glmnet_multistep_fit_impl}
+\title{Predict custom glmnet model}
+\usage{
+\method{predict}{glmnet_multistep_fit_impl}(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+}
+\value{
+predictions
+}
+\description{
+Predict custom glmnet model
+}
+\keyword{internal}

man/predict.mars_multistep_fit_impl.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{predict.mars_multistep_fit_impl}
+\alias{predict.mars_multistep_fit_impl}
+\title{Predict custom mars model}
+\usage{
+\method{predict}{mars_multistep_fit_impl}(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+}
+\value{
+predictions
+}
+\description{
+Predict custom mars model
+}
+\keyword{internal}

man/predict.svm_poly_multistep_fit_impl.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{predict.svm_poly_multistep_fit_impl}
+\alias{predict.svm_poly_multistep_fit_impl}
+\title{Predict custom svm_poly model}
+\usage{
+\method{predict}{svm_poly_multistep_fit_impl}(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+}
+\value{
+predictions
+}
+\description{
+Predict custom svm_poly model
+}
+\keyword{internal}

man/predict.svm_rbf_multistep_fit_impl.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{predict.svm_rbf_multistep_fit_impl}
+\alias{predict.svm_rbf_multistep_fit_impl}
+\title{Predict custom svm_rbf model}
+\usage{
+\method{predict}{svm_rbf_multistep_fit_impl}(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+}
+\value{
+predictions
+}
+\description{
+Predict custom svm_rbf model
+}
+\keyword{internal}

man/predict.xgboost_multistep_fit_impl.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{predict.xgboost_multistep_fit_impl}
+\alias{predict.xgboost_multistep_fit_impl}
+\title{Predict custom xgboost model}
+\usage{
+\method{predict}{xgboost_multistep_fit_impl}(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+}
+\value{
+predictions
+}
+\description{
+Predict custom xgboost model
+}
+\keyword{internal}

man/print.cubist_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{print.cubist_multistep}
+\alias{print.cubist_multistep}
+\title{Print custom cubist model}
+\usage{
+\method{print}{cubist_multistep}(x, ...)
+}
+\value{
+Prints model info
+}
+\description{
+Print custom cubist model
+}
+\keyword{internal}

man/print.cubist_multistep_fit_impl.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{print.cubist_multistep_fit_impl}
+\alias{print.cubist_multistep_fit_impl}
+\title{Print fitted custom cubist model}
+\usage{
+\method{print}{cubist_multistep_fit_impl}(x, ...)
+}
+\value{
+prints custom model
+}
+\description{
+Print fitted custom cubist model
+}
+\keyword{internal}

man/print.glmnet_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{print.glmnet_multistep}
+\alias{print.glmnet_multistep}
+\title{Print custom glmnet model}
+\usage{
+\method{print}{glmnet_multistep}(x, ...)
+}
+\value{
+Prints model info
+}
+\description{
+Print custom glmnet model
+}
+\keyword{internal}

man/print.glmnet_multistep_fit_impl.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{print.glmnet_multistep_fit_impl}
+\alias{print.glmnet_multistep_fit_impl}
+\title{Print fitted custom glmnet model}
+\usage{
+\method{print}{glmnet_multistep_fit_impl}(x, ...)
+}
+\value{
+prints custom model
+}
+\description{
+Print fitted custom glmnet model
+}
+\keyword{internal}

man/print.mars_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{print.mars_multistep}
+\alias{print.mars_multistep}
+\title{Print custom mars model}
+\usage{
+\method{print}{mars_multistep}(x, ...)
+}
+\value{
+Prints model info
+}
+\description{
+Print custom mars model
+}
+\keyword{internal}

man/print.mars_multistep_fit_impl.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{print.mars_multistep_fit_impl}
+\alias{print.mars_multistep_fit_impl}
+\title{Print fitted custom mars model}
+\usage{
+\method{print}{mars_multistep_fit_impl}(x, ...)
+}
+\value{
+prints custom model
+}
+\description{
+Print fitted custom mars model
+}
+\keyword{internal}

man/print.svm_poly_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{print.svm_poly_multistep}
+\alias{print.svm_poly_multistep}
+\title{Print custom svm_poly model}
+\usage{
+\method{print}{svm_poly_multistep}(x, ...)
+}
+\value{
+Prints model info
+}
+\description{
+Print custom svm_poly model
+}
+\keyword{internal}

man/print.svm_poly_multistep_fit_impl.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{print.svm_poly_multistep_fit_impl}
+\alias{print.svm_poly_multistep_fit_impl}
+\title{Print fitted custom svm_poly model}
+\usage{
+\method{print}{svm_poly_multistep_fit_impl}(x, ...)
+}
+\value{
+prints custom model
+}
+\description{
+Print fitted custom svm_poly model
+}
+\keyword{internal}

man/print.svm_rbf_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{print.svm_rbf_multistep}
+\alias{print.svm_rbf_multistep}
+\title{Print custom svm_rbf model}
+\usage{
+\method{print}{svm_rbf_multistep}(x, ...)
+}
+\value{
+Prints model info
+}
+\description{
+Print custom svm_rbf model
+}
+\keyword{internal}

man/print.svm_rbf_multistep_fit_impl.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{print.svm_rbf_multistep_fit_impl}
+\alias{print.svm_rbf_multistep_fit_impl}
+\title{Print fitted custom svm_rbf model}
+\usage{
+\method{print}{svm_rbf_multistep_fit_impl}(x, ...)
+}
+\value{
+prints custom model
+}
+\description{
+Print fitted custom svm_rbf model
+}
+\keyword{internal}

man/print.xgboost_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{print.xgboost_multistep}
+\alias{print.xgboost_multistep}
+\title{Print custom xgboost model}
+\usage{
+\method{print}{xgboost_multistep}(x, ...)
+}
+\value{
+Prints model info
+}
+\description{
+Print custom xgboost model
+}
+\keyword{internal}

man/print.xgboost_multistep_fit_impl.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{print.xgboost_multistep_fit_impl}
+\alias{print.xgboost_multistep_fit_impl}
+\title{Print fitted custom xgboost model}
+\usage{
+\method{print}{xgboost_multistep_fit_impl}(x, ...)
+}
+\value{
+prints custom model
+}
+\description{
+Print fitted custom xgboost model
+}
+\keyword{internal}

man/svm_poly_multistep.Rd

@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{svm_poly_multistep}
+\alias{svm_poly_multistep}
+\title{SVM-POLY Multistep Horizon}
+\usage{
+svm_poly_multistep(
+  mode = "regression",
+  cost = NULL,
+  degree = NULL,
+  scale_factor = NULL,
+  margin = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{mode}{A single character string for the type of model.
+The only possible value for this model is "regression".}
+
+\item{cost}{A positive number for the cost of predicting
+a sample within or on the wrong side of the margin.}
+
+\item{degree}{A positive number for polynomial degree.}
+
+\item{scale_factor}{A positive number for the polynomial
+scaling factor.}
+
+\item{margin}{A positive number for the epsilon in the SVM
+insensitive loss function}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\value{
+Get Multistep Horizon SVM-POLY model
+}
+\description{
+SVM-POLY Multistep Horizon
+}
+\keyword{internal}

man/svm_poly_multistep_fit_impl.Rd

@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{svm_poly_multistep_fit_impl}
+\alias{svm_poly_multistep_fit_impl}
+\title{Bridge SVM-POLY Multistep Modeling function}
+\usage{
+svm_poly_multistep_fit_impl(
+  x,
+  y,
+  C = double(1),
+  degree = integer(1),
+  scale = double(1),
+  epsilon = double(1),
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{x}{A dataframe of xreg (exogenous regressors)}
+
+\item{y}{A numeric vector of values to fit}
+
+\item{C}{A positive number for the cost of predicting
+a sample within or on the wrong side of the margin.}
+
+\item{degree}{A positive number for polynomial degree.}
+
+\item{scale}{A positive number for the polynomial
+scaling factor.}
+
+\item{epsilon}{A positive number for the epsilon in the SVM
+insensitive loss function}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\description{
+Bridge SVM-POLY Multistep Modeling function
+}
+\keyword{internal}

man/svm_poly_multistep_predict_impl.Rd

@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{svm_poly_multistep_predict_impl}
+\alias{svm_poly_multistep_predict_impl}
+\title{Bridge prediction Function for SVM-POLY Multistep Horizon Models}
+\usage{
+svm_poly_multistep_predict_impl(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+
+\item{...}{Additional \code{parsnip}-related options, depending on the
+value of \code{type}. Arguments to the underlying model's prediction
+function cannot be passed here (use the \code{opts} argument instead).
+Possible arguments are:
+\itemize{
+\item \code{interval}: for \code{type} equal to \code{"survival"} or \code{"quantile"}, should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type} equal to \code{"conf_int"}, \code{"pred_int"}, or \code{"survival"},
+this is the parameter for the tail area of the intervals
+(e.g. confidence level for confidence intervals).
+Default value is \code{0.95}.
+\item \code{std_error}: for \code{type} equal to \code{"conf_int"} or \code{"pred_int"}, add
+the standard error of fit or prediction (on the scale of the
+linear predictors). Default value is \code{FALSE}.
+\item \code{quantile}: for \code{type} equal to \code{quantile}, the quantiles of the
+distribution. Default is \code{(1:9)/10}.
+\item \code{eval_time}: for \code{type} equal to \code{"survival"} or \code{"hazard"}, the
+time points at which the survival probability or hazard is estimated.
+}}
+}
+\value{
+predictions
+}
+\description{
+Bridge prediction Function for SVM-POLY Multistep Horizon Models
+}
+\keyword{internal}

man/svm_rbf_multistep.Rd

@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{svm_rbf_multistep}
+\alias{svm_rbf_multistep}
+\title{SVM-RBF Multistep Horizon}
+\usage{
+svm_rbf_multistep(
+  mode = "regression",
+  cost = NULL,
+  rbf_sigma = NULL,
+  margin = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{mode}{A single character string for the type of model.
+The only possible value for this model is "regression".}
+
+\item{cost}{A positive number for the cost of predicting
+a sample within or on the wrong side of the margin.}
+
+\item{rbf_sigma}{A positive number for radial basis function.}
+
+\item{margin}{A positive number for the epsilon in the SVM
+insensitive loss function.}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\value{
+Get Multistep Horizon SVM-RBF model
+}
+\description{
+SVM-RBF Multistep Horizon
+}
+\keyword{internal}

man/svm_rbf_multistep_fit_impl.Rd

@@ -0,0 +1,43 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{svm_rbf_multistep_fit_impl}
+\alias{svm_rbf_multistep_fit_impl}
+\title{Bridge SVM-RBF Multistep Modeling function}
+\usage{
+svm_rbf_multistep_fit_impl(
+  x,
+  y,
+  C = double(1),
+  sigma = integer(1),
+  epsilon = double(1),
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{x}{A dataframe of xreg (exogenous regressors)}
+
+\item{y}{A numeric vector of values to fit}
+
+\item{C}{A positive number for the cost of predicting
+a sample within or on the wrong side of the margin.}
+
+\item{sigma}{A positive number for radial basis function.}
+
+\item{epsilon}{A positive number for the epsilon in the SVM
+insensitive loss function}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\description{
+Bridge SVM-RBF Multistep Modeling function
+}
+\keyword{internal}

man/svm_rbf_multistep_predict_impl.Rd

@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{svm_rbf_multistep_predict_impl}
+\alias{svm_rbf_multistep_predict_impl}
+\title{Bridge prediction Function for SVM-RBF Multistep Horizon Models}
+\usage{
+svm_rbf_multistep_predict_impl(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+
+\item{...}{Additional \code{parsnip}-related options, depending on the
+value of \code{type}. Arguments to the underlying model's prediction
+function cannot be passed here (use the \code{opts} argument instead).
+Possible arguments are:
+\itemize{
+\item \code{interval}: for \code{type} equal to \code{"survival"} or \code{"quantile"}, should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type} equal to \code{"conf_int"}, \code{"pred_int"}, or \code{"survival"},
+this is the parameter for the tail area of the intervals
+(e.g. confidence level for confidence intervals).
+Default value is \code{0.95}.
+\item \code{std_error}: for \code{type} equal to \code{"conf_int"} or \code{"pred_int"}, add
+the standard error of fit or prediction (on the scale of the
+linear predictors). Default value is \code{FALSE}.
+\item \code{quantile}: for \code{type} equal to \code{quantile}, the quantiles of the
+distribution. Default is \code{(1:9)/10}.
+\item \code{eval_time}: for \code{type} equal to \code{"survival"} or \code{"hazard"}, the
+time points at which the survival probability or hazard is estimated.
+}}
+}
+\value{
+predictions
+}
+\description{
+Bridge prediction Function for SVM-RBF Multistep Horizon Models
+}
+\keyword{internal}

man/translate.cubist_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{translate.cubist_multistep}
+\alias{translate.cubist_multistep}
+\title{Translate custom cubist model}
+\usage{
+\method{translate}{cubist_multistep}(x, engine = x$engine, ...)
+}
+\value{
+translated model
+}
+\description{
+Translate custom cubist model
+}
+\keyword{internal}

man/translate.glmnet_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{translate.glmnet_multistep}
+\alias{translate.glmnet_multistep}
+\title{Translate custom glmnet model}
+\usage{
+\method{translate}{glmnet_multistep}(x, engine = x$engine, ...)
+}
+\value{
+translated model
+}
+\description{
+Translate custom glmnet model
+}
+\keyword{internal}

man/translate.mars_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{translate.mars_multistep}
+\alias{translate.mars_multistep}
+\title{Translate custom mars model}
+\usage{
+\method{translate}{mars_multistep}(x, engine = x$engine, ...)
+}
+\value{
+translated model
+}
+\description{
+Translate custom mars model
+}
+\keyword{internal}

man/translate.svm_poly_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{translate.svm_poly_multistep}
+\alias{translate.svm_poly_multistep}
+\title{Translate custom svm_poly model}
+\usage{
+\method{translate}{svm_poly_multistep}(x, engine = x$engine, ...)
+}
+\value{
+translated model
+}
+\description{
+Translate custom svm_poly model
+}
+\keyword{internal}

man/translate.svm_rbf_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{translate.svm_rbf_multistep}
+\alias{translate.svm_rbf_multistep}
+\title{Translate custom svm_rbf model}
+\usage{
+\method{translate}{svm_rbf_multistep}(x, engine = x$engine, ...)
+}
+\value{
+translated model
+}
+\description{
+Translate custom svm_rbf model
+}
+\keyword{internal}

man/translate.xgboost_multistep.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{translate.xgboost_multistep}
+\alias{translate.xgboost_multistep}
+\title{Translate custom xgboost model}
+\usage{
+\method{translate}{xgboost_multistep}(x, engine = x$engine, ...)
+}
+\value{
+translated model
+}
+\description{
+Translate custom xgboost model
+}
+\keyword{internal}

man/update.cubist_multistep.Rd

@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_cubist.R
+\name{update.cubist_multistep}
+\alias{update.cubist_multistep}
+\title{Update parameter in custom cubist model}
+\usage{
+\method{update}{cubist_multistep}(
+  object,
+  parameters = NULL,
+  committees = NULL,
+  neighbors = NULL,
+  max_rules = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL,
+  fresh = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{model object}
+
+\item{parameters}{parameters}
+
+\item{committees}{committees}
+
+\item{neighbors}{neighbors}
+
+\item{max_rules}{max rules}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+
+\item{fresh}{fresh}
+
+\item{...}{extra args passed to cubist}
+}
+\value{
+Updated model
+}
+\description{
+Update parameter in custom cubist model
+}
+\keyword{internal}

man/update.glmnet_multistep.Rd

@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_glmnet.R
+\name{update.glmnet_multistep}
+\alias{update.glmnet_multistep}
+\title{Update parameter in custom glmnet model}
+\usage{
+\method{update}{glmnet_multistep}(
+  object,
+  parameters = NULL,
+  mixture = NULL,
+  penalty = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL,
+  fresh = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{model object}
+
+\item{parameters}{parameters}
+
+\item{mixture}{mixture}
+
+\item{penalty}{penalty}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+
+\item{fresh}{fresh}
+
+\item{...}{extra args passed to glmnet}
+}
+\value{
+Updated model
+}
+\description{
+Update parameter in custom glmnet model
+}
+\keyword{internal}

man/update.mars_multistep.Rd

@@ -0,0 +1,51 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_mars.R
+\name{update.mars_multistep}
+\alias{update.mars_multistep}
+\title{Update parameter in custom mars model}
+\usage{
+\method{update}{mars_multistep}(
+  object,
+  parameters = NULL,
+  num_terms = NULL,
+  prod_degree = NULL,
+  prune_method = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL,
+  fresh = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{model object}
+
+\item{parameters}{parameters}
+
+\item{num_terms}{The number of features that will be retained in
+the final model, including the intercept.}
+
+\item{prod_degree}{The highest possible interaction degree.}
+
+\item{prune_method}{The pruning method.}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+
+\item{fresh}{fresh}
+
+\item{...}{extra args passed to mars}
+}
+\value{
+Updated model
+}
+\description{
+Update parameter in custom mars model
+}
+\keyword{internal}

man/update.svm_poly_multistep.Rd

@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_poly.R
+\name{update.svm_poly_multistep}
+\alias{update.svm_poly_multistep}
+\title{Update parameter in custom svm_poly model}
+\usage{
+\method{update}{svm_poly_multistep}(
+  object,
+  parameters = NULL,
+  cost = NULL,
+  degree = NULL,
+  scale_factor = NULL,
+  margin = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  selected_features = NULL,
+  fresh = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{model object}
+
+\item{parameters}{parameters}
+
+\item{cost}{A positive number for the cost of predicting
+a sample within or on the wrong side of the margin.}
+
+\item{degree}{A positive number for polynomial degree.}
+
+\item{scale_factor}{A positive number for the polynomial
+scaling factor.}
+
+\item{margin}{A positive number for the epsilon in the SVM
+insensitive loss function}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{selected_features}{selected features}
+
+\item{fresh}{fresh}
+
+\item{...}{extra args passed to svm_poly}
+}
+\value{
+Updated model
+}
+\description{
+Update parameter in custom svm_poly model
+}
+\keyword{internal}

man/update.svm_rbf_multistep.Rd

@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_svm_rbf.R
+\name{update.svm_rbf_multistep}
+\alias{update.svm_rbf_multistep}
+\title{Update parameter in custom svm_rbf model}
+\usage{
+\method{update}{svm_rbf_multistep}(
+  object,
+  parameters = NULL,
+  cost = NULL,
+  rbf_sigma = NULL,
+  margin = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  selected_features = NULL,
+  fresh = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{model object}
+
+\item{parameters}{parameters}
+
+\item{cost}{A positive number for the cost of predicting
+a sample within or on the wrong side of the margin.}
+
+\item{rbf_sigma}{A positive number for radial basis function.}
+
+\item{margin}{A positive number for the epsilon in the SVM
+insensitive loss function.}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{selected_features}{selected features}
+
+\item{fresh}{fresh}
+
+\item{...}{extra args passed to svm_rbf}
+}
+\value{
+Updated model
+}
+\description{
+Update parameter in custom svm_rbf model
+}
+\keyword{internal}

man/update.xgboost_multistep.Rd

@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{update.xgboost_multistep}
+\alias{update.xgboost_multistep}
+\title{Update parameter in custom xgboost model}
+\usage{
+\method{update}{xgboost_multistep}(
+  object,
+  parameters = NULL,
+  mtry = NULL,
+  trees = NULL,
+  min_n = NULL,
+  tree_depth = NULL,
+  learn_rate = NULL,
+  loss_reduction = NULL,
+  sample_size = NULL,
+  stop_iter = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL,
+  fresh = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{model object}
+
+\item{parameters}{parameters}
+
+\item{mtry}{mtry}
+
+\item{trees}{trees}
+
+\item{min_n}{min_n}
+
+\item{tree_depth}{tree depth}
+
+\item{learn_rate}{learn rate}
+
+\item{loss_reduction}{loss reduction}
+
+\item{sample_size}{number for the number (or proportion) of data that is exposed to the fitting routine.}
+
+\item{stop_iter}{The number of iterations without improvement before stopping}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+
+\item{fresh}{fresh}
+
+\item{...}{extra args passed to xgboost}
+}
+\value{
+Updated model
+}
+\description{
+Update parameter in custom xgboost model
+}
+\keyword{internal}

man/xgboost_multistep.Rd

@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{xgboost_multistep}
+\alias{xgboost_multistep}
+\title{XGBOOST Multistep Horizon}
+\usage{
+xgboost_multistep(
+  mode = "regression",
+  mtry = NULL,
+  trees = NULL,
+  min_n = NULL,
+  tree_depth = NULL,
+  learn_rate = NULL,
+  loss_reduction = NULL,
+  sample_size = NULL,
+  stop_iter = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL
+)
+}
+\arguments{
+\item{mode}{A single character string for the type of model.
+The only possible value for this model is "regression".}
+
+\item{mtry}{mtry}
+
+\item{trees}{trees}
+
+\item{min_n}{min_n}
+
+\item{tree_depth}{tree depth}
+
+\item{learn_rate}{learn rate}
+
+\item{loss_reduction}{loss reduction}
+
+\item{sample_size}{number for the number (or proportion) of data that is exposed to the fitting routine.}
+
+\item{stop_iter}{The number of iterations without improvement before stopping}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+}
+\value{
+Get Multistep Horizon XGBoost model
+}
+\description{
+XGBOOST Multistep Horizon
+}
+\keyword{internal}

man/xgboost_multistep_fit_impl.Rd

@@ -0,0 +1,75 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{xgboost_multistep_fit_impl}
+\alias{xgboost_multistep_fit_impl}
+\title{Bridge XGBOOST Multistep Modeling function}
+\usage{
+xgboost_multistep_fit_impl(
+  x,
+  y,
+  max_depth = 6,
+  nrounds = 15,
+  eta = 0.3,
+  colsample_bytree = NULL,
+  colsample_bynode = NULL,
+  min_child_weight = 1,
+  gamma = 0,
+  subsample = 1,
+  validation = 0,
+  early_stop = NULL,
+  lag_periods = NULL,
+  external_regressors = NULL,
+  forecast_horizon = NULL,
+  selected_features = NULL,
+  ...
+)
+}
+\arguments{
+\item{x}{A dataframe of xreg (exogenous regressors)}
+
+\item{y}{A numeric vector of values to fit}
+
+\item{max_depth}{An integer for the maximum depth of the tree.}
+
+\item{nrounds}{An integer for the number of boosting iterations.}
+
+\item{eta}{A numeric value between zero and one to control the learning rate.}
+
+\item{colsample_bytree}{Subsampling proportion of columns.}
+
+\item{colsample_bynode}{Subsampling proportion of columns for each node
+within each tree. See the \code{counts} argument below. The default uses all
+columns.}
+
+\item{min_child_weight}{A numeric value for the minimum sum of instance
+weights needed in a child to continue to split.}
+
+\item{gamma}{A number for the minimum loss reduction required to make a
+further partition on a leaf node of the tree}
+
+\item{subsample}{Subsampling proportion of rows.}
+
+\item{validation}{A positive number. If on \verb{[0, 1)} the value, \code{validation}
+is a random proportion of data in \code{x} and \code{y} that are used for performance
+assessment and potential early stopping. If 1 or greater, it is the \emph{number}
+of training set samples use for these purposes.}
+
+\item{early_stop}{An integer or \code{NULL}. If not \code{NULL}, it is the number of
+training iterations without improvement before stopping. If \code{validation} is
+used, performance is base on the validation set; otherwise the training set
+is used.}
+
+\item{lag_periods}{lag periods}
+
+\item{external_regressors}{external regressors}
+
+\item{forecast_horizon}{forecast horizon}
+
+\item{selected_features}{selected features}
+
+\item{...}{Additional arguments passed to \code{xgboost::xgb.train}}
+}
+\description{
+Bridge XGBOOST Multistep Modeling function
+}
+\keyword{internal}

man/xgboost_multistep_predict_impl.Rd

@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multistep_xgboost.R
+\name{xgboost_multistep_predict_impl}
+\alias{xgboost_multistep_predict_impl}
+\title{Bridge prediction Function for XGBOOST Multistep Horizon Models}
+\usage{
+xgboost_multistep_predict_impl(object, new_data, ...)
+}
+\arguments{
+\item{object}{model object}
+
+\item{new_data}{input data to predict}
+
+\item{...}{Additional arguments passed to \code{predict.xgb.Booster()}}
+}
+\value{
+predictions
+}
+\description{
+Bridge prediction Function for XGBOOST Multistep Horizon Models
+}
+\keyword{internal}