Conditional Inference Procedures in a Permutation Test Framework

Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in .

Generalized Linear Models Extended

Extended techniques for generalized linear models (GLMs), especially for binary responses, including parametric links and heteroscedastic latent variables.

Generalized Additive Models for Location Scale and Shape

Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), . The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.

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 tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) , Zeileis et al. (2008) and Strobl et al. (2007) .

Fitting and Testing Generalized Logistic Distributions

Tools for the generalized logistic distribution (Type I, also known as skew-logistic distribution), encompassing basic distribution functions (p, q, d, r, score), maximum likelihood estimation, and structural change methods.

Truncated Gaussian Regression Models

Estimation of models for truncated Gaussian variables by maximum likelihood.

Visualizing Categorical Data

Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

Tools and Classes for Statistical Models

A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome!

A Toolkit for Recursive Partytioning

A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) < https://jmlr.org/papers/v16/hothorn15a.html>.

R Fortunes

A collection of fortunes from the R community.