[doc] Reference the R doc in sphinx document site.

.github/workflows/jvm_tests.yml

@@ -10,6 +10,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
+  PR_COMMIT_SHA: ${{ github.event.pull_request.head.sha }}
   BRANCH_NAME: >-
     ${{ github.event.pull_request.number && 'PR-' }}${{ github.event.pull_request.number || github.ref_name }}
 
@@ -122,7 +123,7 @@ jobs:
         run: |
           python3 ops/pipeline/manage-artifacts.py upload \
             --s3-bucket xgboost-docs \
-            --prefix ${BRANCH_NAME}/${GITHUB_SHA} --make-public \
+            --prefix ${BRANCH_NAME}/${PR_COMMIT_SHA} --make-public \
             jvm-packages/${{ env.BRANCH_NAME }}.tar.bz2
 
   build-test-jvm-packages:

.github/workflows/r_tests.yml

@@ -4,6 +4,9 @@ on: [push, pull_request]
 
 env:
   GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+  PR_COMMIT_SHA: ${{ github.event.pull_request.head.sha }}
+  BRANCH_NAME: >-
+    ${{ github.event.pull_request.number && 'PR-' }}${{ github.event.pull_request.number || github.ref_name }}
 
 permissions:
   contents: read # to fetch code (actions/checkout)
@@ -12,6 +15,7 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
+
 jobs:
   test-Rpkg:
     runs-on: ${{ matrix.os }}
@@ -101,3 +105,25 @@ jobs:
         if: steps.changes.outputs.r_package == 'true'
         run: |
           python3 ops/script/test_r_package.py --r=/usr/bin/R --task=doc
+
+  build-r-docs:
+    name: Build docs for the R package
+    runs-on:
+      - runs-on=${{ github.run_id }}
+      - runner=linux-amd64-cpu
+      - tag=r-tests-build-docs
+    steps:
+      # Restart Docker daemon so that it recognizes the ephemeral disks
+      - run: sudo systemctl restart docker
+      - uses: actions/checkout@v4
+        with:
+          submodules: "true"
+      - name: Log into Docker registry (AWS ECR)
+        run: bash ops/pipeline/login-docker-registry.sh
+      - run: bash ops/pipeline/build-r-docs.sh
+      - name: Upload R doc
+        run: |
+          python3 ops/pipeline/manage-artifacts.py upload \
+            --s3-bucket xgboost-docs \
+            --prefix ${BRANCH_NAME}/${PR_COMMIT_SHA} --make-public \
+            r-docs-${{ env.BRANCH_NAME }}.tar.bz2

R-package/.Rbuildignore

@@ -6,3 +6,6 @@
 README.md
 ^doc$
 ^Meta$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$

R-package/.gitignore

@@ -0,0 +1 @@
+docs

R-package/README.md

@@ -1,13 +1,13 @@
-XGBoost R Package for Scalable GBM
-==================================
+XGBoost R Package
+=================
 
 [![CRAN Status Badge](http://www.r-pkg.org/badges/version/xgboost)](https://cran.r-project.org/web/packages/xgboost)
 [![CRAN Downloads](http://cranlogs.r-pkg.org/badges/xgboost)](https://cran.rstudio.com/web/packages/xgboost/index.html)
-[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](http://xgboost.readthedocs.org/en/latest/R-package/index.html)
+[![Documentation Status](https://readthedocs.org/projects/xgboost/badge/?version=latest)](https://xgboost.readthedocs.org/en/latest/R-package/index.html)
 
 Resources
 ---------
-* [XGBoost R Package Online Documentation](http://xgboost.readthedocs.org/en/latest/R-package/index.html)
+* [XGBoost R Package Online Documentation](https://xgboost.readthedocs.org/en/stable/R-package/index.html)
   - Check this out for detailed documents, examples and tutorials.
 
 Installation
@@ -19,13 +19,7 @@ We are [on CRAN](https://cran.r-project.org/web/packages/xgboost/index.html) now
 install.packages('xgboost')
 ```
 
-For more detailed installation instructions, please see [here](http://xgboost.readthedocs.org/en/latest/build.html#r-package-installation).
-
-Examples
---------
-
-* Please visit [walk through example](demo).
-* See also the [example scripts](../demo/kaggle-higgs) for Kaggle Higgs Challenge, including [speedtest script](../demo/kaggle-higgs/speedtest.R) on this dataset and the one related to [Otto challenge](../demo/kaggle-otto), including a [RMarkdown documentation](../demo/kaggle-otto/understandingXGBoostModel.Rmd).
+For more detailed installation instructions, please see [here](https://xgboost.readthedocs.io/en/stable/install.html).
 
 Development
 -----------

R-package/pkgdown/_pkgdown.yml

@@ -0,0 +1,93 @@
+url: https://github.com/dmlc/xgboost
+
+template:
+  bootstrap: 5
+  math-rendering: mathjax
+
+reference:
+  - title: High Level Interface
+    desc: High level XGBoost interface
+    contents:
+      - "xgboost"
+      - "predict.xgboost"
+      - "print.xgboost"
+  - title: Datasets
+    desc: Test datasets bundled with the R package.
+    contents:
+      - "agaricus.train"
+      - "agaricus.test"
+  - title: Global Configuration
+    desc: Global configuration for the XGBoost library.
+    contents:
+      - "xgb.config"
+      - "xgb.set.config"
+      - "xgb.get.config"
+  - title: DMatrix
+    desc: Low-level data storage.
+    contents:
+      - "xgb.DMatrix"
+      - "xgb.DMatrix.hasinfo"
+      - "xgb.DMatrix.save"
+      - "dim.xgb.DMatrix"
+      - "dimnames.xgb.DMatrix"
+      - "print.xgb.DMatrix"
+      - "xgb.DataBatch"
+      - "xgb.DataIter"
+      - "xgb.get.DMatrix.data"
+      - "xgb.get.DMatrix.num.non.missing"
+      - "xgb.ExtMemDMatrix"
+      - "xgb.QuantileDMatrix.from_iterator"
+      - "xgb.get.DMatrix.qcut"
+      - "xgb.slice.DMatrix"
+  - title: Booster
+    desc: The model for XGBoost.
+    contents:
+      - "a-compatibility-note-for-saveRDS-save"
+      - "coef.xgb.Booster"
+      - "getinfo.xgb.Booster"
+      - "predict.xgb.Booster"
+      - "print.xgb.Booster"
+      - "xgb.load"
+      - "xgb.load.raw"
+      - "xgb.save"
+      - "xgb.save.raw"
+      - "xgb.copy.Booster"
+      - "xgb.slice.Booster"
+      - "xgb.get.num.boosted.rounds"
+      - "xgb.is.same.Booster"
+      - "xgb.importance"
+      - "xgb.attr"
+      - "xgb.create.features"
+      - "xgb.model.dt.tree"
+      - "xgb.model.parameters<-"
+      - "xgb.ggplot.deepness"
+      - "xgb.dump"
+      - "variable.names.xgb.Booster"
+      - "xgb.ggplot.importance"
+      - "xgb.plot.multi.trees"
+      - "xgb.plot.shap"
+      - "xgb.ggplot.shap.summary"
+      - "xgb.plot.tree"
+      - "xgb.gblinear.history"
+  - title: Training Callbacks
+    desc: Callback functions used for training.
+    contents:
+      - "xgb.Callback"
+      - "xgb.cb.cv.predict"
+      - "xgb.cb.early.stop"
+      - "xgb.cb.evaluation.log"
+      - "xgb.cb.gblinear.history"
+      - "xgb.cb.print.evaluation"
+      - "xgb.cb.reset.parameters"
+      - "xgb.cb.save.model"
+  - title: Low-level Training Functions
+    desc: Low-level Training Functions with DMatrix and Booster
+    contents:
+      - "xgb.params"
+      - "xgb.train"
+      - "xgb.cv"
+      - "print.xgb.cv.synchronous"
+  - title: Deprecation Settings
+    contents:
+      - "xgboost-deprecated"
+      - "xgboost-options"

R-package/tests/helper_scripts/install_deps.R

@@ -26,7 +26,6 @@ pkgs <- c(
   "RhpcBLASctl",
   ## imports
   "Matrix",
-  "methods",
   "data.table",
   "jsonlite"
 )

R-package/vignettes/xgboost_introduction.Rmd

@@ -12,7 +12,10 @@ output:
         toc_float: true
 ---
 
-# Introduction
+XGBoost for R introduction
+==========================
+
+## Introduction
 
 **XGBoost** is an optimized distributed gradient boosting library designed to be highly **efficient**, **flexible** and **portable**. It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
 
@@ -22,7 +25,7 @@ For more details about XGBoost's features and usage, see the [online documentati
 
 This short vignette outlines the basic usage of the R interface for XGBoost, assuming the reader has some familiarity with the underlying concepts behind statistical modeling with gradient-boosted decision trees.
 
-# Building a predictive model
+## Building a predictive model
 
 At its core, XGBoost consists of a C++ library which offers bindings for different programming languages, including R. The R package for XGBoost provides an idiomatic interface similar to those of other statistical modeling packages using and x/y design, as well as a lower-level interface that interacts more directly with the underlying core library and which is similar to those of other language bindings like Python, plus various helpers to interact with its model objects such as by plotting their feature importances or converting them to other formats.
 
@@ -62,7 +65,7 @@ model_abserr <- xgboost(x, y, objective = "reg:absoluteerror", nthreads = 1, nro
 
 _Note: the objective must match with the type of the "y" response variable - for example, classification objectives for discrete choices require "factor" types, while regression models for real-valued data require "numeric" types._
 
-# Model parameters
+## Model parameters
 
 XGBoost models allow a large degree of control over how they are built. By their nature, gradient-boosted decision tree ensembles are able to capture very complex patterns between features in the data and a response variable, which also means they can suffer from overfitting if not controlled appropirately.
 
@@ -105,7 +108,7 @@ xgboost(
 )
 ```
 
-# Examining model objects
+## Examining model objects
 
 XGBoost model objects for the most part consist of a pointer to a C++ object where most of the information is held and which is interfaced through the utility functions and methods in the package, but also contains some R attributes that can be retrieved (and new ones added) through `attributes()`:
 
@@ -131,7 +134,7 @@ xgb.importance(model)
 xgb.model.dt.tree(model)
 ```
 
-# Other features
+## Other features
 
 XGBoost supports many additional features on top of its traditional gradient-boosting framework, including, among others:
 
@@ -143,7 +146,7 @@ XGBoost supports many additional features on top of its traditional gradient-boo
 
 See the [online documentation](https://xgboost.readthedocs.io/en/stable/index.html) - particularly the [tutorials section](https://xgboost.readthedocs.io/en/stable/tutorials/index.html) - for a glimpse over further functionalities that XGBoost offers.
 
-# The low-level interface
+## The low-level interface
 
 In addition to the `xgboost(x, y, ...)` function, XGBoost also provides a lower-level interface for creating model objects through the function `xgb.train()`, which resembles the same `xgb.train` functions in other language bindings of XGBoost.
 

doc/R-package/.gitignore

@@ -1 +1,2 @@
 *~
+*.md

doc/R-package/Makefile

@@ -5,6 +5,8 @@ PKGROOT=../../R-package
 xgboostfromJSON.md: $(PKGROOT)/vignettes/xgboostfromJSON.Rmd
 xgboost_introduction.md: $(PKGROOT)/vignettes/xgboost_introduction.Rmd
 
+all: xgboostfromJSON.md xgboost_introduction.md
+
 # General Rules for build rmarkdowns, need knitr
 %.md:
 	Rscript -e \

doc/R-package/index.rst

@@ -9,19 +9,32 @@ XGBoost R Package
 
 You have found the XGBoost R Package!
 
+.. toctree::
+  :maxdepth: 2
+  :titlesonly:
+
 ***********
 Get Started
 ***********
+
 * Checkout the :doc:`Installation Guide </install>` contains instructions to install xgboost, and :doc:`Tutorials </tutorials/index>` for examples on how to use XGBoost for various tasks.
-* Read the `API documentation <https://cran.r-project.org/web/packages/xgboost/xgboost.pdf>`_.
+* Read the latest `API documentation <../r_docs/R-package/docs/reference/index.html>`__ . This might refer to a newer version than the one on CRAN.
+* Read the `CRAN documentation <https://cran.r-project.org/web/packages/xgboost/xgboost.pdf>`_.
+
+*********
+Vignettes
+*********
+
+.. toctree::
+
+  xgboost_introduction
+  xgboostfromJSON
 
 ************
 Other topics
 ************
 
 .. toctree::
-  :maxdepth: 2
-  :titlesonly:
 
   Handling of indexable elements <index_base>
   Developer guide: parameters from core library <adding_parameters>

doc/R-package/r_docs/index.rst

@@ -0,0 +1,5 @@
+:orphan:
+
+=============
+XGBoost R API
+=============