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), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Version: 2.5-0
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, dagitty
LinkingTo: Rcpp (≥ 0.11.0), RcppArmadillo, BH
Suggests: MASS, Matrix, Rgraphviz, mvtnorm
Published: 2017-07-12
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], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [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


Reference manual: pcalg.pdf
Vignettes: Causal Inference: The R package pcalg
Package source: pcalg_2.5-0.tar.gz
Windows binaries: r-devel: pcalg_2.5-0.zip, r-release: pcalg_2.5-0.zip, r-oldrel: pcalg_2.5-0.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: pcalg_2.5-0.tgz
Old sources: pcalg archive

Reverse dependencies:

Reverse depends: qtlnet
Reverse imports: BiDAG, kpcalg, SID
Reverse suggests: backShift, CompareCausalNetworks, ParallelPC


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