Package: pcalg
Version: 2.0-1
Date: 2014-02-26
Author: Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler
Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>
Title: Methods for graphical models and causal inference
Description: 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.
Depends: R (>= 2.15.0)
LinkingTo: Rcpp, RcppArmadillo, BH
Imports: graphics, utils, methods, abind, graph, RBGL, igraph, ggm,
        corpcor, robustbase, vcd, Rcpp
Suggests: MASS, Matrix, Rgraphviz, mvtnorm, sfsmisc
ByteCompile: yes
NeedsCompilation: yes
Encoding: UTF-8
License: GPL (>= 2)
URL: http://pcalg.r-forge.r-project.org/
Repository: CRAN
Repository/R-Forge/Project: pcalg
Repository/R-Forge/Revision: 253
Repository/R-Forge/DateTimeStamp: 2014-03-06 08:38:21
Date/Publication: 2014-03-06 08:38:21
Packaged: 2014-03-06 11:39:52 UTC; rforge
