pcalg: Methods for Graphical Models and Causal Inference

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 data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.

Version: 2.2-4
Depends: R (≥ 3.0.2)
Imports: stats, graphics, utils, methods, abind, graph, RBGL, igraph, ggm, corpcor, robustbase, vcd, Rcpp, bdsmatrix, sfsmisc (≥ 1.0-26), fastICA, clue, gmp
LinkingTo: Rcpp (≥ 0.11.0), RcppArmadillo, BH
Suggests: MASS, Matrix, Rgraphviz, mvtnorm
Published: 2015-07-23
Author: Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb]
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
Package source: pcalg_2.2-4.tar.gz
Windows binaries: r-devel: pcalg_2.2-4.zip, r-release: pcalg_2.2-4.zip, r-oldrel: pcalg_2.2-4.zip
OS X Snow Leopard binaries: r-release: not available, r-oldrel: pcalg_2.2-0.tgz
OS X Mavericks binaries: r-release: pcalg_2.2-4.tgz
Old sources: pcalg archive

Reverse dependencies:

Reverse depends: qtlnet
Reverse imports: backShift, SID
Reverse suggests: CompareCausalNetworks, MXM