Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).

Maximum Likelihood estimation of random utility discrete choice models (logit and probit).

A wide range of functions for phylogenetic analysis. Functionality is concentrated in phylogenetic comparative biology, but also includes a diverse array of methods for visualizing, manipulating, reading or writing, and even inferring phylogenetic ...

Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" <doi:10.1214/aos/1176347963>. (The term "MARS" is trademarked and thus not used in the name of the package.)

Functions for drawing and calibrating samples.

Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.

Fitting linear models and generalized linear models to large data sets by updating algorithms.

Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.

Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline.

Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit.

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are ...