The gRapHD package is designed for efficient selection of high-dimensional undirected graphical models. The package provides tools for selecting trees, forests and decomposable models minimising information criteria such as AIC or BIC, and for displaying the independence graphs of the models. It also has some useful tools for analysing graphical structures. It supports discrete, continuous, or both types of variables.
The R-package postHoc implements methods for post hoc pairwise comparisons and clustering for standard linear models, generalised linear models, mixed models, generalised linear mixed models. It is under implementation methods using some non-parametric tests (Kruskal-Wallis and permutation tests applied to compare the distribution of sub-samples defined by the levels of a classification factor) and some contingency table-related models. The package constructs groups and clusters of parameters that are not statistically significantly different from each other (at a pre-specified significance level) using a graph-based representation where the vertices are the model parameters (typically the levels of an explanatory classification variable) and two vertices are adjacent in the graph if, and only if, they are not statistically significantly different. The groups of parameters are the maximal cliques in the graph described above; the clusters are formed by finding the largest subgraph contained in the representation graph. A vertice of the graph can belong to more than one group, but it belongs to only one cluster. The package postHoc has methods defined for plotting the representation graph, produces tables with estimates, confidence intervals (bootstrap or Wald) and grouping/clustering, interaction line plots and bar plots.
The R-package testModelDistribution implements tools for testing the subjacent distribution behind Generalised Linear Models and Generalised Linear Mixed Models. The package supply tests for purely discrete distributions (using concordance between the expected and the observed frequencies), continuous distributions (using the probability integral transformation or bootstrap tests using several distances in the space of distributions), and distributions of mixed type (e.g., the gamma compound Poisson distribution).