R package abn

abn: An R package to providing routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data.

Webpage http://r-bayesian-networks.org.  A stable version of the package is posted on CRAN. 

R package varrank

varrank: Heuristics Tools Based on Mutual Information for Variable Ranking. A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.

Webpage (based on pkgdown); a stable version of the package is posted on CRAN.

R package mcmcabn

mcmcabn: Flexible implementation of a structural MCMC sampler for Directed Acyclic Graphs (DAGs). It supports new edge reversal move and the Markov blanket resampling from as well as three priors: a prior controlling for structure complexity, an uninformative prior and a user defined prior. The three main problems that can be addressed by this R package are selecting the most probable structure based on a cache of pre-computed scores, controlling for overfitting and sampling the landscape of high scoring structures.

Webpage (based on pkgdown); a stable version of the package is posted on CRAN, a development version is posted here.

R package tsabn

tsabn is an R package that extends the abn R package for time series analysis. This is a machine learning approach to empirically identifying associations in complex and high dimensional datasets of time series.

A current version of the package is available here.