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

Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), ...

Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis ...

Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method.

Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of ...

E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing ...

Circular Statistics, from "Topics in Circular Statistics" (2001) S. Rao Jammalamadaka and A.

Classification and regression trees.

Contains: 1) Facilities for working with grouped data: 'do' something to data stratified 'by' some variables. 2) LSmeans (least-squares means), general linear contrasts.

A collection of miscellaneous 3d plots, including isosurfaces.

Various R programming tools for model fitting.