pcalg: Methods for graphical models and causal inferenceThis 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 |
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