Multivariate Adaptive Regression Spline Models: Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines". (The term "MARS" is [...]

Model-Based Boosting: Functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing [...]

A Laboratory for Recursive Partytioning: A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed [...]

Bayesian treed Gaussian process models: Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model (LLM). Special [...]

Recursive Partitioning: Recursive partitioning and regression trees

Classification and Regression Training: Misc functions for training and plotting classification and regression models

Breiman and Cutler's random forests for classification and regression: Classification and regression based on a forest of trees using random inputs.

R/Weka interface: An R interface to Weka (Version 3.5.8). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, [...]

L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model: A package for fitting possibly high dimensional penalized regression models. The penalty structure can be any combination [...]

L1 constrained estimation aka `lasso': Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. (1998)

Classification and Regression Training LSF Style: Augment some caret functions for parallel processing

Classification and Regression Training in Parallel Using NetworkSpaces: Augment some caret functions using parallel processing

Relevant Dimension Estimation (RDE) in Feature Spaces: The package provides functions for estimating the relevant dimension of a data set in feature spaces, applications to model selection, [...]

Ishwaran and Kogalur's Random Survival Forest: Ensemble survival analysis based on a random forest of trees using random inputs.

VR: Functions and datasets to support Venables and Ripley, 'Modern Applied Statistics with S' (4th edition).

Improved Predictors: Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

Classification and visualization: Miscellaneous functions for classification and visualization developed at the Fakultaet Statistik, Technische Universitaet Dortmund

Mining Association Rules and Frequent Itemsets: Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also [...]

Variable selection using random forests: Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection [...]

Classification rule based on Bayesian naive Bayes models with feature selection bias corrected: This software is used to predict the binary response based on high dimensional features, for example [...]