Add missing Node help. fix some help typos.

DESCRIPTION

@@ -66,5 +66,5 @@ Config/testthat/parallel: false
 Config/testthat/start-first: interface, explain, params
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Language: en-US

R/hardhat.R

@@ -8,6 +8,8 @@
 #'   * A __matrix__ of predictors.
 #'   * A __recipe__ specifying a set of preprocessing steps
 #'     created from [recipes::recipe()].
+#'   * A __Node__ where tree will be used as hierarchical outcome,
+#'     and attributes will be used as predictors.
 #'
 #'  The predictor data should be standardized (e.g. centered or scaled).
 #'  The model treats categorical predictors internally thus, you don't need to
@@ -244,6 +246,8 @@ new_tabnet_fit <- function(fit, blueprint) {
 #'   * A __matrix__ of predictors.
 #'   * A __recipe__ specifying a set of preprocessing steps
 #'     created from [recipes::recipe()].
+#'   * A __Node__ where tree will be used as hierarchical outcome,
+#'     and attributes will be used as predictors.
 #'
 #'  The predictor data should be standardized (e.g. centered or scaled).
 #'  The model treats categorical predictors internally thus, you don't need to

R/model.R

@@ -83,7 +83,7 @@ resolve_data <- function(x, y) {
 #' @param learn_rate initial learning rate for the optimizer.
 #' @param optimizer the optimization method. currently only `"adam"` is supported,
 #'   you can also pass any torch optimizer function.
-#' @param valid_split (`[0, 1)`) The fraction of the dataset used for validation.
+#' @param valid_split In \[0, 1). The fraction of the dataset used for validation.
 #'   (default = 0 means no split)
 #' @param num_independent Number of independent Gated Linear Units layers at each step of the encoder.
 #'   Usual values range from 1 to 5.

R/tab-network.R

@@ -418,7 +418,7 @@ tabnet_no_embedding <- torch::nn_module(
 #' @param n_shared Number of shared GLU layer in each GLU block of the encoder.
 #' @param epsilon Avoid log(0), this should be kept very low.
 #' @param virtual_batch_size Batch size for Ghost Batch Normalization.
-#' @param momentum  Float value between 0 and 1 which will be used for momentum in all batch norm.
+#' @param momentum  Numerical value between 0 and 1 which will be used for momentum in all batch norm.
 #' @param mask_type Either "sparsemax" or "entmax" : this is the masking function to use.
 #' @export
 tabnet_nn <- torch::nn_module(

tests/spelling.R

@@ -0,0 +1,3 @@
+if(requireNamespace('spelling', quietly = TRUE))
+  spelling::spell_check_test(vignettes = TRUE, error = FALSE,
+                             skip_on_cran = TRUE)