bump dev version, add keyword internal

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: modeltime.gluonts
 Type: Package
 Title: 'GluonTS' Deep Learning
-Version: 0.3.1
+Version: 0.3.1.9000
 Authors@R: c(
     person("Matt", "Dancho", email = "[email protected]", role = c("aut", "cre")),
     person("Business Science", role = "cph"))
@@ -12,7 +12,7 @@ Description:
     (<https://ts.gluon.ai/index.html>).
 License: MIT + file LICENSE
 Encoding: UTF-8
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.3
 Roxygen: list(markdown = TRUE)
 Depends:
     modeltime (>= 0.6.1)

R/parsnip-deepar.R

@@ -439,6 +439,8 @@ translate.deep_ar <- function(x, engine = x$engine, ...) {
 #' @param scale_by_id Scales numeric data by `id` group using mean = 0, standard deviation = 1 transformation. (default: FALSE)
 #' @param ... Additional parameters passed to `gluonts.model.deepar.DeepAREstimator()`
 #'
+#' @keywords internal
+#'
 #' @export
 deepar_fit_impl <- function(x, y, freq, prediction_length, id,
 
@@ -662,6 +664,8 @@ print.deepar_fit_impl <- function(x, ...) {
 #' @param num_feat_static_real Number of static numeric features
 #' @param ... Parameters passed to `pytorch_lightning.trainer.Trainer()`
 #'
+#' @keywords internal
+#'
 #' @export
 deepar_torch_fit_impl <- function(x, y, freq, prediction_length, id,
 
@@ -908,6 +912,8 @@ deepar_torch_fit_impl <- function(x, y, freq, prediction_length, id,
 #'
 #' @inheritParams parsnip::predict.model_fit
 #'
+#' @keywords internal
+#'
 #' @export
 deepar_predict_impl <- function(object, new_data) {
 

R/parsnip-deepstate.R

@@ -387,6 +387,8 @@ translate.deep_state <- function(x, engine = x$engine, ...) {
 #'
 #' @param ... Additional parameters passed to `gluonts.model.deepstate.DeepStateEstimator()`
 #'
+#' @keywords internal
+#'
 #' @export
 deepstate_fit_impl <- function(x, y, freq, prediction_length, id,
 
@@ -598,6 +600,8 @@ print.deepstate_fit_impl <- function(x, ...) {
 #'
 #' @inheritParams parsnip::predict.model_fit
 #'
+#' @keywords internal
+#'
 #' @export
 deepstate_predict_impl <- function(object, new_data) {
 

R/parsnip-gp_forecaster.R

@@ -379,6 +379,8 @@ translate.gp_forecaster <- function(x, engine = x$engine, ...) {
 #' @param scale_by_id Scales numeric data by `id` group using mean = 0, standard deviation = 1 transformation. (default: FALSE)
 #' @param ... Additional parameters passed to `gluonts.model.gp_forecaster.GP ForecasterEstimator()`
 #'
+#' @keywords internal
+#'
 #' @export
 gp_forecaster_fit_impl <- function(x, y, freq, prediction_length, id,
 
@@ -572,6 +574,8 @@ print.gp_forecaster_fit_impl <- function(x, ...) {
 #'
 #' @inheritParams parsnip::predict.model_fit
 #'
+#' @keywords internal
+#'
 #' @export
 gp_forecaster_predict_impl <- function(object, new_data) {
 

R/parsnip-nbeats.R

@@ -422,6 +422,8 @@ translate.nbeats <- function(x, engine = x$engine, ...) {
 #' @param loss_function The loss function (also known as metric) to use for training the network. Unlike other models in GluonTS this network does not use a distribution. One of the following: "sMAPE", "MASE" or "MAPE". The default value is "MAPE".
 #' @param ... Additional Arguments passed to `gluonts.model.n_beats.NBEATSEstimator()`
 #'
+#' @keywords internal
+#'
 #' @export
 nbeats_fit_impl <- function(x, y, freq, prediction_length, id,
 
@@ -623,6 +625,8 @@ print.nbeats_fit_impl <- function(x, ...) {
 #'
 #' @inheritParams parsnip::predict.model_fit
 #'
+#' @keywords internal
+#'
 #' @export
 nbeats_predict_impl <- function(object, new_data) {
 
@@ -703,6 +707,8 @@ predict.nbeats_fit_impl <- function(object, new_data, ...) {
 #'
 #' `meta_context_length x meta_loss_function x meta_bagging_size`
 #'
+#' @keywords internal
+#'
 #' @export
 nbeats_ensemble_fit_impl <- function(x, y, freq, prediction_length, id,
 
@@ -906,6 +912,8 @@ print.nbeats_ensemble_fit_impl <- function(x, ...) {
 #'
 #' @inheritParams parsnip::predict.model_fit
 #'
+#' @keywords internal
+#'
 #' @export
 nbeats_ensemble_predict_impl <- function(object, new_data) {