Several addon 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
: Singlehiddenlayer neural network are
implemented in package
nnet
(shipped with base R).
Package
RSNNS
offers an interface to the Stuttgart
Neural Network Simulator (SNNS).

Recursive Partitioning
: Treestructured 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 CARTlike
trees.
A rich toolbox of partitioning algorithms is available in
Weka
,
package
RWeka
provides an interface to this
implementation, including the J4.8variant of C4.5 and M5.
The
Cubist
package fits rulebased models (similar
to trees) with linear regression models in the terminal leaves,
instancebased corrections and boosting. The
C50
package can fit
C5.0 classification trees, rulebased models, and boosted versions of these.
Two recursive partitioning algorithms with unbiased variable
selection and statistical stopping criterion are implemented in
package
party. Function
ctree()
is based on
nonparametrical 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
as well.
An adaptation of
rpart
for multivariate responses
is available in package
mvpart. For problems with binary input variables
the package
LogicReg
implements logic regression.
Graphical tools for the visualization of
trees are available in package
maptree.
Trees for modelling longitudinal data by means of
random effects is offered by package
REEMtree.
Partitioning of mixture models is performed by
RPMM.
Computational infrastructure for representing trees and
unified methods for predition and visualization is implemented
in
partykit.
This infrastructure is used by package
evtree
to implement evolutionary learning
of globally optimal trees.
Oblique trees are available in package
oblique.tree.

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.
The
varSelRF
and
Boruta
packages focus on variable selection by means
for random forest algorithms. For large data sets, package
bigrf
computes random forests in parallel and uses large memory objects
to store the data.

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 elasticnet 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
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.
The shrunken
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. Variable selection through clone selection
in SVMs in penalized models (SCAD or L1 penalties) is implemented
in package
penalizedSVM. Various forms of
penalized discriminant analysis are implemented in
packages
hda,
rda, 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.
An implementation of bundle methods for regularized risk minimization
is available form package
bmrm.

Boosting
: Various forms of gradient boosting are
implemented in package
gbm
(treebased functional gradient
descent boosting). The Hingeloss is optimized by the boosting implementation
in package
bst. Package
GAMBoost
can be used to fit generalized additive models
by a boosting algorithm. An extensible boosting framework for
generalized linear, additive and nonparametric models is available in
package
mboost. Likelihoodbased boosting for Cox models
is implemented in
CoxBoost
and for mixed models in
GMMBoost.
GAMLSS models can be fitted using boosting by
gamboostLSS.

Support Vector Machines and Kernel Methods
: The function
svm()
from
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 oneagainstall classification) is provided in package
klaR.
The relevant dimension in kernel feature spaces can be estimated
using
rdetools
which also offers procedures for model selection
and prediction.

Bayesian Methods
:
Bayesian nonstationary, semiparametric nonlinear regression
and design by treed Gaussian processes including Bayesian CART and
treed linear models are made available by package
tgp.

Optimization using Genetic Algorithms
:
Packages
rgp
and
rgenoud
offer 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 realvalued
parameter optimization.

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.

Fuzzy Rulebased Systems
:
Package
frbs
implements a host of standard
methods for learning fuzzy rulebased 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
package.

Model selection and validation
: Package
e1071
has function
tune()
for hyper parameter tuning and
function
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.
Functions for ROC analysis and other visualisation techniques
for comparing candidate classifiers are available from package
ROCR.
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).

Elements of Statistical Learning
: Data sets, functions and
examples from the book
The Elements of Statistical Learning: Data Mining,
Inference, and Prediction
by Trevor Hastie, Robert Tibshirani and
Jerome Friedman have been packaged and are available as
ElemStatLearn.

GUI
rattle
is a graphical user interface for data mining in R.
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.