pcalg: Methods for graphical models and causal inference

This package contains several functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of observational and interventional data without hidden variables). For causal inference the IDA algorithm and the generalized backdoor criterion is implemented.

Version:	2.0-3
Depends:	R (≥ 3.0.2)
Imports:	graphics, utils, methods, abind, graph, RBGL, igraph, ggm, corpcor, robustbase, vcd, Rcpp
LinkingTo:	Rcpp (≥ 0.11.0), RcppArmadillo, BH
Suggests:	MASS, Matrix, Rgraphviz, mvtnorm, sfsmisc
Published:	2014-07-01
Author:	Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler
Maintainer:	Markus Kalisch <kalisch at stat.math.ethz.ch>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	http://pcalg.r-forge.r-project.org/
NeedsCompilation:	yes
Citation:	pcalg citation info
Materials:	NEWS ChangeLog
In views:	gR
CRAN checks:	pcalg results

Downloads:

Reference manual:	pcalg.pdf
Vignettes:	Causal Inference: The R package pcalg
Package source:	pcalg_2.0-3.tar.gz
Windows binaries:	r-devel: pcalg_2.0-3.zip, r-release: pcalg_2.0-3.zip, r-oldrel: pcalg_2.0-3.zip
OS X Snow Leopard binaries:	r-release: pcalg_2.0-3.tgz, r-oldrel: pcalg_2.0-3.tgz
OS X Mavericks binaries:	r-release: pcalg_2.0-3.tgz
Old sources:	pcalg archive