[docs] [R-package] fix HTML generation errors in R docs, update to roxygen2 7.3.3 (#7074)

* [docs] [R-package] fix HTML generation errors in R docs, update to roxygen2 7.3.3

* fix conda env

* Update R-package/R/lgb.Dataset.R
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update docs

---------
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

R-package/DESCRIPTION

@@ -63,4 +63,4 @@ Imports:
     utils
 SystemRequirements:
     C++17
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3

R-package/R/lgb.Booster.R

@@ -874,6 +874,7 @@ Booster <- R6::R6Class(
 )
 
 #' @name lgb_predict_shared_params
+#' @title Shared prediction parameter docs
 #' @param type Type of prediction to output. Allowed types are:\itemize{
 #'             \item \code{"response"}: will output the predicted score according to the objective function being
 #'                   optimized (depending on the link function that the objective uses), after applying any necessary
@@ -915,6 +916,8 @@ Booster <- R6::R6Class(
 #'               the "Predict Parameters" section of the documentation} for a list of parameters and
 #'               valid values. Where these conflict with the values of keyword arguments to this function,
 #'               the values in \code{params} take precedence.
+#' @details This page contains shared documentation for prediction-related parameters used throughout the package.
+#' @keywords internal
 NULL
 
 #' @name predict.lgb.Booster

R-package/R/lgb.Dataset.R

@@ -10,6 +10,7 @@
 #'              \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups,
 #'              where the first 10 records are in the first group, records 11-30 are in the
 #'              second group, etc.
+#' @details This page contains shared documentation for dataset-related parameters used throughout the package.
 #' @keywords internal
 NULL
 

R-package/man/lgb_predict_shared_params.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/lgb.Booster.R
+\name{lgb_predict_shared_params}
+\alias{lgb_predict_shared_params}
+\title{Shared prediction parameter docs}
+\arguments{
+\item{type}{Type of prediction to output. Allowed types are:\itemize{
+            \item \code{"response"}: will output the predicted score according to the objective function being
+                  optimized (depending on the link function that the objective uses), after applying any necessary
+                  transformations - for example, for \code{objective="binary"}, it will output class probabilities.
+            \item \code{"class"}: for classification objectives, will output the class with the highest predicted
+                  probability. For other objectives, will output the same as "response". Note that \code{"class"} is
+                  not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function
+                  for more details).
+            \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations'
+                  results) from which the "response" number is produced for a given objective function - for example,
+                  for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as
+                  "regression", since no transformation is applied, the output will be the same as for "response".
+            \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls
+                  in each tree in the model, outputted as integers, with one column per tree.
+            \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an
+                  intercept (each feature will produce one column).
+            }
+
+            Note that, if using custom objectives, types "class" and "response" will not be available and will
+            default towards using "raw" instead.
+
+            If the model was fit through function \link{lightgbm} and it was passed a factor as labels,
+            passing the prediction type through \code{params} instead of through this argument might
+            result in factor levels for classification objectives not being applied correctly to the
+            resulting output.
+
+            \emph{New in version 4.0.0}}
+
+\item{start_iteration}{int or None, optional (default=None)
+Start index of the iteration to predict.
+If None or <= 0, starts from the first iteration.}
+
+\item{num_iteration}{int or None, optional (default=None)
+Limit number of iterations in the prediction.
+If None, if the best iteration exists and start_iteration is None or <= 0, the
+best iteration is used; otherwise, all iterations from start_iteration are used.
+If <= 0, all iterations from start_iteration are used (no limits).}
+
+\item{params}{a list of additional named parameters. See
+\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{
+the "Predict Parameters" section of the documentation} for a list of parameters and
+valid values. Where these conflict with the values of keyword arguments to this function,
+the values in \code{params} take precedence.}
+}
+\description{
+Shared prediction parameter docs
+}
+\details{
+This page contains shared documentation for prediction-related parameters used throughout the package.
+}
+\keyword{internal}

R-package/man/lgb_shared_dataset_params.Rd

@@ -20,4 +20,7 @@ second group, etc.}
 \description{
 Parameter docs for fields used in \code{lgb.Dataset} construction
 }
+\details{
+This page contains shared documentation for dataset-related parameters used throughout the package.
+}
 \keyword{internal}

docs/env.yml

@@ -6,14 +6,14 @@ dependencies:
   - breathe>=4.36
   - doxygen>=1.13.2
   - python=3.12
-  - r-base>=4.3.3
-  - r-data.table=1.16.4
-  - r-jsonlite=1.8.9
-  - r-knitr=1.49
-  - r-markdown=1.13
-  - r-matrix=1.6_5
-  - r-pkgdown=2.1.1
-  - r-roxygen2=7.3.2
+  - r-base>=4.5.1
+  - r-data.table=1.17.8
+  - r-jsonlite=2.0.0
+  - r-knitr=1.50
+  - r-markdown=2.0
+  - r-matrix=1.7_4
+  - r-pkgdown=2.1.3
+  - r-roxygen2=7.3.3
   # skipping scikit-learn 1.7.1 because of the problems described in https://github.com/microsoft/LightGBM/issues/6978
   - scikit-learn>=1.6.1,!=1.7.1
   - sphinx>=8.1.3