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

Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.

Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...

Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions.

Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

Data analysis, linear models and differential expression for microarray data.

Functions and Datasets to Accompany J. Fox and S.

Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ...

Functions for kernel smoothing (and density estimation) corresponding to the book: Wand, M.P. and Jones, M.C.

Model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.