First attempt (#16010)

h2o-bindings/bin/gen_R.py

@@ -284,6 +284,8 @@ def get_help(param, indent=0):
     phelp = param.get('help')
     if not phelp:
         return
+    # fix braces so that CRAN doesn't complain
+    phelp = phelp.replace("{", "\{").replace("}", "\}")
     if ptype == 'boolean':
         phelp = "\code{Logical}. " + phelp
     if pvalues:

h2o-r/h2o-package/R/frame.R

@@ -816,7 +816,7 @@ h2o.unique <- function(x, include_nas=FALSE){
 #'        interval notation.
 #' @param include.lowest \code{Logical}, indicationg if an 'x[i]' equal to the lowest (or highest, for \code{right =
 #'        FALSE} 'breaks' value should be included
-#' @param right /code{Logical}, indicating if the intervals should be closed on the right (opened on the left) or vice
+#' @param right \code{Logical}, indicating if the intervals should be closed on the right (opened on the left) or vice
 #'        versa.
 #' @param dig.lab Integer which is used when labels are not given, determines the number of digits used in formatting
 #'        the break numbers.

h2o-r/h2o-package/R/gam.R

@@ -16,7 +16,7 @@
 #'        If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.
 #' @param training_frame Id of the training data frame.
 #' @param gam_columns Arrays of predictor column names for gam for smoothers using single or multiple predictors like
-#'        {{'c1'},{'c2','c3'},{'c4'},...}
+#'        \{\{'c1'\},\{'c2','c3'\},\{'c4'\},...\}
 #' @param model_id Destination id for this model; auto-generated if not specified.
 #' @param validation_frame Id of the validation data frame.
 #' @param nfolds Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.

h2o-r/h2o-package/R/models.R

@@ -1065,7 +1065,7 @@ h2o.predict_contributions <- predict_contributions.H2OModel
 #'          8         0         1         1
 #'          9         1         0         0
 #' 
-#' Where 1 in the tree_{number} cols means row is used in the tree and 0 means that row is not used.
+#' Where 1 in the tree_\{number\} cols means row is used in the tree and 0 means that row is not used.
 #' The structure of the output depends on sample_rate or sample_size parameter setup.
 #'
 #' Note: Multinomial classification generate tree for each category, each tree use the same sample of the data.

h2o-r/h2o-package/R/randomforest.R

@@ -66,7 +66,7 @@
 #'        Defaults to -1 (time-based random number).
 #' @param build_tree_one_node \code{Logical}. Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
 #'        Defaults to FALSE.
-#' @param mtries Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for
+#' @param mtries Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt\{p\} for
 #'        classification and p/3 for regression (where p is the # of predictors Defaults to -1.
 #' @param sample_rate Row sample rate per tree (from 0.0 to 1.0) Defaults to 0.632.
 #' @param sample_rate_per_class A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree

h2o-r/h2o-package/R/upliftrandomforest.R

@@ -32,7 +32,7 @@
 #' @param max_runtime_secs Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
 #' @param seed Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default).
 #'        Defaults to -1 (time-based random number).
-#' @param mtries Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for
+#' @param mtries Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt\{p\} for
 #'        classification and p/3 for regression (where p is the # of predictors Defaults to -2.
 #' @param sample_rate Row sample rate per tree (from 0.0 to 1.0) Defaults to 0.632.
 #' @param sample_rate_per_class A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree