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Package: pcalg
Version: 2.2-4
Date: 2015-07-22
Title: Methods for Graphical Models and Causal Inference
Description: 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.
Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>
Authors@R: c(person("Markus","Kalisch",
email="kalisch@stat.math.ethz.ch", role=c("aut","cre")),
person("Alain", "Hauser", role="aut"), person("Martin","Maechler", role="aut"),
person("Diego", "Colombo", role="ctb"), person("Doris", "Entner", role="ctb"),
person("Patrik","Hoyer", role="ctb"), person("Antti", "Hyttinen", role="ctb"),
person("Jonas", "Peters", role="ctb"))
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]
Depends: R (>= 3.0.2)
LinkingTo: Rcpp (>= 0.11.0), RcppArmadillo, BH
Imports: stats, graphics, utils, methods, abind, graph, RBGL, igraph,
ggm, corpcor, robustbase, vcd, Rcpp, bdsmatrix, sfsmisc (>=
1.0-26), fastICA, clue, gmp
Suggests: MASS, Matrix, Rgraphviz, mvtnorm
ByteCompile: yes
NeedsCompilation: yes
Encoding: UTF-8
License: GPL (>= 2)
URL: http://pcalg.r-forge.r-project.org/
Packaged: 2015-07-22 14:53:53 UTC; maechler
Repository: CRAN
Date/Publication: 2015-07-23 09:21:52
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