Typos

Author: abichat

R/aaa_models.R

@@ -277,7 +277,7 @@ check_interface_val <- function(x) {
 #' @param eng A single character string for the model engine.
 #' @param arg A single character string for the model argument name.
 #' @param has_submodel A single logical for whether the argument
-#'  can make predictions on mutiple submodels at once.
+#'  can make predictions on multiple submodels at once.
 #' @param func A named character vector that describes how to call
 #'  a function. `func` should have elements `pkg` and `fun`. The
 #'  former is optional but is recommended and the latter is

R/aaa_multi_predict.R

@@ -44,7 +44,7 @@ predict.model_spec <- function(object, ...) {
 #'
 #' `has_multi_predict()` tests to see if an object can make multiple
 #'  predictions on submodels from the same object. `multi_predict_args()`
-#'  returns the names of the argments to `multi_predict()` for this model
+#'  returns the names of the arguments to `multi_predict()` for this model
 #'  (if any).
 #' @param object An object to test.
 #' @param ... Not currently used.

R/boost_tree.R

@@ -30,22 +30,22 @@
 #' @param mode A single character string for the type of model.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param mtry An number for the number (or proportion) of predictors that will
+#' @param mtry A number for the number (or proportion) of predictors that will
 #'  be randomly sampled at each split when creating the tree models (`xgboost`
 #'  only).
 #' @param trees An integer for the number of trees contained in
 #'  the ensemble.
 #' @param min_n An integer for the minimum number of data points
 #'  in a node that are required for the node to be split further.
-#' @param tree_depth An integer for the maximum deopth of the tree (i.e. number
+#' @param tree_depth An integer for the maximum depth of the tree (i.e. number
 #'  of splits) (`xgboost` only).
 #' @param learn_rate A number for the rate at which the boosting algorithm adapts
 #'   from iteration-to-iteration (`xgboost` only).
 #' @param loss_reduction A number for the reduction in the loss function required
 #'   to split further  (`xgboost` only).
-#' @param sample_size An number for the number (or proportion) of data that is
+#' @param sample_size A number for the number (or proportion) of data that is
 #'  exposed to the fitting routine. For `xgboost`, the sampling is done at at
-#'  each iteration while `C5.0` samples once during traning.
+#'  each iteration while `C5.0` samples once during training.
 #' @details
 #' The data given to the function are not saved and are only used
 #'  to determine the _mode_ of the model. For `boost_tree()`, the
@@ -277,7 +277,7 @@ check_args.boost_tree <- function(object) {
 #' @param colsample_bytree Subsampling proportion of columns.
 #' @param min_child_weight A numeric value for the minimum sum of instance
 #'  weights needed in a child to continue to split.
-#' @param gamma An number for the minimum loss reduction required to make a
+#' @param gamma A number for the minimum loss reduction required to make a
 #'  further partition on a leaf node of the tree
 #' @param subsample Subsampling proportion of rows.
 #' @param ... Other options to pass to `xgb.train`.

R/descriptors.R

@@ -12,7 +12,7 @@
 #'   \item `.obs()`: The current number of rows in the data set.
 #'   \item `.preds()`: The number of columns in the data set that are
 #'     associated with the predictors prior to dummy variable creation.
-#'   \item `.cols()`: The number of predictor columns availible after dummy
+#'   \item `.cols()`: The number of predictor columns available after dummy
 #'     variables are created (if any).
 #'   \item `.facts()`: The number of factor predictors in the dat set.
 #'   \item `.lvls()`: If the outcome is a factor, this is a table

R/linear_reg.R

@@ -19,7 +19,7 @@
 #' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "regression".
-#' @param penalty An non-negative number representing the total
+#' @param penalty A non-negative number representing the total
 #'  amount of regularization (`glmnet`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization
 #'  (aka weight decay) while the other models can be a combination
@@ -338,7 +338,7 @@ predict_raw._elnet <- function(object, new_data, opts = list(), ...)  {
 #' @importFrom tidyr gather
 #' @export
 #'@rdname multi_predict
-#' @param penalty An numeric vector of penalty values.
+#' @param penalty A numeric vector of penalty values.
 multi_predict._elnet <-
   function(object, new_data, type = NULL, penalty = NULL, ...) {
     if (any(names(enquos(...)) == "newdata"))

R/logistic_reg.R

@@ -19,7 +19,7 @@
 #' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "classification".
-#' @param penalty An non-negative number representing the total
+#' @param penalty A non-negative number representing the total
 #'  amount of regularization (`glmnet`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization
 #'  (aka weight decay) while the other models can be a combination

R/multinom_reg.R

@@ -19,7 +19,7 @@
 #' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "classification".
-#' @param penalty An non-negative number representing the total
+#' @param penalty A non-negative number representing the total
 #'  amount of regularization (`glmnet`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization
 #'  (aka weight decay) while the other models can be a combination

R/nullmodel.R

@@ -128,7 +128,7 @@ predict.nullmodel <- function (object, new_data = NULL, type  = NULL, ...) {
 #' General Interface for null models
 #'
 #' `null_model()` is a way to generate a _specification_ of a model before
-#'  fitting and allows the model to be created using R. It doens't have any
+#'  fitting and allows the model to be created using R. It doesn't have any
 #'  main arguments.
 #'
 #' @param mode A single character string for the type of model.