Last updated on 2021-07-12
by Torsten Hothorn
Several add-on packages implement ideas and methods developed at the
borderline between computer science and statistics - this field of research
is usually referred to as machine learning.
The packages can be roughly structured into the following topics:
- Neural Networks and Deep Learning: Single-hidden-layer neural network are
implemented in package nnet (shipped with base R).
Package RSNNS offers an interface to the Stuttgart
Neural Network Simulator (SNNS).
Packages implementing deep learning flavours of neural networks
include deepnet (feed-forward neural network,
restricted Boltzmann machine, deep belief network, stacked
autoencoders), RcppDL (denoising autoencoder,
stacked denoising autoencoder, restricted Boltzmann machine,
deep belief network) and h2o
(feed-forward neural network, deep autoencoders). An interface
to tensorflow is
available in tensorflow. The torch
package implements an interface to the libtorch library.
- Recursive Partitioning: Tree-structured models for
regression, classification and survival analysis, following the
ideas in the CART book, are
implemented in rpart (shipped with base R) and tree.
Package rpart is recommended for computing CART-like
A rich toolbox of partitioning algorithms is available in
package RWeka provides an interface to this
implementation, including the J4.8-variant of C4.5 and M5.
The Cubist package fits rule-based models (similar
to trees) with linear regression models in the terminal leaves,
instance-based corrections and boosting. The C50 package can fit
C5.0 classification trees, rule-based models, and boosted versions of these.
Two recursive partitioning algorithms with unbiased variable
selection and statistical stopping criterion are implemented in
package party and partykit. Function
ctree() is based on
non-parametric conditional inference procedures for testing
independence between response and each input variable whereas
mob() can be used to partition parametric models.
Extensible tools for visualizing binary trees
and node distributions of the response are available in package
party and partykit as well.
Graphical tools for the visualization of
trees are available in package maptree.
Partitioning of mixture models is performed by RPMM.
Computational infrastructure for representing trees and
unified methods for prediction and visualization is implemented
This infrastructure is used by package evtree to implement evolutionary learning
of globally optimal trees. Survival trees are available in
- Random Forests: The reference implementation of the random
forest algorithm for regression and classification is available in
package randomForest. Package ipred has bagging
for regression, classification and survival analysis as well as
bundling, a combination of multiple models via
ensemble learning. In addition, a random forest variant for
response variables measured at arbitrary scales based on
conditional inference trees is implemented in package party.
randomForestSRC implements a unified treatment of Breiman's random forests for
survival, regression and classification problems. Quantile regression forests quantregForest
allow to regress quantiles of a numeric response on exploratory
variables via a random forest approach. For binary data,
The varSelRF and Boruta packages focus on variable selection by means
for random forest algorithms. In addition, packages ranger and Rborist
offer R interfaces to fast C++ implementations of random forests.
Reinforcement Learning Trees, featuring splits in variables
which will be important down the tree, are implemented in
package RLT. wsrf implements an
alternative variable weighting method for variable subspace selection
in place of the traditional random variable sampling. Package
RGF is an interface to a Python implementation of
a procedure called regularized greedy forests.
Random forests for parametric models, including forests for the
estimation of predictive distributions, are available in
packages trtf (predictive transformation forests,
possibly under censoring and trunction) and grf
(an implementation of generalised random forests).
- Regularized and Shrinkage Methods: Regression models with some
constraint on the parameter estimates can be fitted with the
lasso2 and lars packages. Lasso with
simultaneous updates for groups of parameters (groupwise lasso)
is available in package grplasso; the
grpreg package implements a number of other group
penalization models, such as group MCP and group SCAD.
The L1 regularization path for generalized linear models and
Cox models can be obtained from functions available in package
glmpath, the entire lasso or elastic-net regularization path (also in elasticnet)
for linear regression,
logistic and multinomial regression models can be obtained from package glmnet.
The penalized package provides
an alternative implementation of lasso (L1) and ridge (L2)
penalized regression models (both GLM and Cox models). Package biglasso fits
Gaussian and logistic linear models under L1 penalty when the data
can't be stored in RAM.
Package RXshrink can be used to identify and display TRACEs
for a specified shrinkage path and to determine the appropriate extent of shrinkage.
Semiparametric additive hazards models under lasso penalties are offered
by package ahaz.
A generalisation of the Lasso shrinkage technique for linear regression
is called relaxed lasso and is available in package relaxo.
Fisher's LDA projection with an optional LASSO penalty to produce sparse
solutions is implemented in package penalizedLDA.
centroids classifier and utilities for gene expression analyses are
implemented in package pamr. An implementation
of multivariate adaptive regression splines is available
in package earth. Various forms of
penalized discriminant analysis are implemented in
packages hda and sda.
Package LiblineaR offers an interface to
the LIBLINEAR library.
The ncvreg package fits linear and logistic
regression models under the the SCAD and MCP
regression penalties using a coordinate descent algorithm. The
same penalties are also implemented in the picasso
An implementation of bundle methods for regularized risk minimization
is available form package bmrm. The Lasso under non-Gaussian and
heteroscedastic errors is estimated by hdm,
inference on low-dimensional components of Lasso regression and of estimated treatment
effects in a high-dimensional setting are also contained. Package SIS
implements sure independence screening in generalised linear and Cox models.
- Boosting and Gradient Descent: Various forms of gradient boosting are
implemented in package gbm (tree-based functional gradient
descent boosting). Package xgboost implements
tree-based boosting using efficient trees as base learners for
several and also user-defined objective functions.
Package lightgbm provides an interface to the
The Hinge-loss is optimized by the boosting implementation
in package bst. An extensible boosting framework for
generalized linear, additive and nonparametric models is available in
package mboost. Likelihood-based boosting for mixed models
is implemented in GMMBoost.
GAMLSS models can be fitted using boosting by gamboostLSS.
An implementation of various learning algorithms based on
Gradient Descent for dealing with regression tasks is available
in package gradDescent.
- Support Vector Machines and Kernel Methods: The function
e1071 offers an interface to the LIBSVM library and
package kernlab implements a flexible framework
for kernel learning (including SVMs, RVMs and other kernel
learning algorithms). An interface to the SVMlight implementation
(only for one-against-all classification) is provided in package
The relevant dimension in kernel feature spaces can be estimated
using rdetools which also offers procedures for model selection
- Bayesian Methods: Bayesian Additive Regression Trees (BART),
where the final model is defined in terms of the sum over
many weak learners (not unlike ensemble methods),
are implemented in packages BayesTree, BART, and
Bayesian nonstationary, semiparametric nonlinear regression
and design by treed Gaussian processes including Bayesian CART and
treed linear models are made available by package tgp.
Bayesian structure learning in undirected graphical models for multivariate continuous, discrete, and mixed data
is implemented in package BDgraph; corresponding
methods relying on spike-and-slab priors are available
from package ssgraph. Naive Bayes classifiers are
available in naivebayes.
- Optimization using Genetic Algorithms:
Package rgenoud offers optimization routines based on genetic algorithms.
The package Rmalschains implements memetic algorithms
with local search chains, which are a special type of
evolutionary algorithms, combining a steady state genetic
algorithm with local search for real-valued
- Association Rules: Package
arules provides both data structures for efficient
handling of sparse binary data as well as interfaces to
implementations of Apriori and Eclat for mining
frequent itemsets, maximal frequent itemsets, closed
frequent itemsets and association rules. Package
opusminer provides an
interface to the OPUS Miner algorithm (implemented in C++) for finding the key associations in
transaction data efficiently, in the form of self-sufficient itemsets, using either leverage or lift.
- Fuzzy Rule-based Systems:
Package frbs implements a host of standard
methods for learning fuzzy rule-based systems from data
for regression and classification. Package RoughSets provides comprehensive implementations of the
rough set theory (RST) and the fuzzy rough set theory (FRST) in a single
- Model selection and validation: Package e1071
tune() for hyper parameter tuning and
errorest() (ipred) can be used for
error rate estimation. The cost parameter C for support vector
machines can be chosen utilizing the functionality of package
svmpath. Data splitting for crossvalidation
and other resampling schemes is available in the
Functions for ROC analysis and other visualisation techniques
for comparing candidate classifiers are available from package
Packages hdi and stabs implement stability
selection for a range of models, hdi
also offers other inference procedures in high-dimensional models.
- Causal Machine Learning: The package DoubleML is an object-oriented
implementation of the double machine learning framework in a variety of causal models. Building
upon the mlr3 ecosystem, estimation of causal effects can be based on an extensive collection of
machine learning methods.
- Other procedures: Evidential classifiers quantify the uncertainty about the
class of a test pattern using a Dempster-Shafer mass function in package evclass.
The OneR (One Rule) package offers a classification algorithm with
enhancements for sophisticated handling of missing values and numeric data
together with extensive diagnostic functions.
- Meta packages:
Package caret provides miscellaneous functions
for building predictive models, including parameter tuning
and variable importance measures. The package can be used
with various parallel implementations (e.g. MPI, NWS etc).
In a similar spirit, packages mlr3 and
mlr3proba offer high-level
to various statistical and machine learning packages. Package
SuperLearner implements a similar toolbox.
The h2o package implements a general purpose machine learning
platform that has scalable implementations of many popular algorithms such
as random forest, GBM, GLM (with elastic net regularization), and deep
learning (feedforward multilayer networks), among others.
An interface to the mlpack C++ library is available from
package mlpack. CORElearn implements a rather broad class of machine learning
algorithms, such as nearest neighbors, trees, random forests, and
several feature selection methods. Similar, package rminer interfaces
several learning algorithms implemented in other packages and computes
several performance measures.
- GUIrattle is a graphical user interface for data mining in R.
- Visualisation (initially contributed by Brandon Greenwell)
stats::termplot() function package can be used to plot the
terms in a model whose predict method supports
The effects package provides graphical and tabular effect
displays for models with a linear predictor (e.g., linear and generalized
linear models). Friedman’s partial dependence plots (PDPs), that are low
dimensional graphical renderings of the prediction function, are implemented
in a few packages. gbm, randomForest and
randomForestSRC provide their own functions for displaying PDPs,
but are limited to the models fit with those packages (the function
partialPlot from randomForest is more limited since
it only allows for one predictor at a time). Packages pdp,
plotmo, and ICEbox are more general and allow for the
creation of PDPs for a wide variety of machine learning models (e.g., random
forests, support vector machines, etc.); both pdp and
plotmo support multivariate displays (plotmo is
limited to two predictors while pdp uses trellis graphics to
display PDPs involving three predictors). By default, plotmo
fixes the background variables at their medians (or first level for factors)
which is faster than constructing PDPs but incorporates less information.
ICEbox focuses on constructing individual conditional expectation
(ICE) curves, a refinement over Friedman's PDPs. ICE curves, as well as
centered ICE curves can also be constructed with the
function from the pdp package. ggRandomForests
provides ggplot2-based tools for the graphical exploration of random forest
models (e.g., variable importance plots and PDPs) from the
randomForest and randomForestSRC packages.