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: |
2.4-3 |
| 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: |
2016-09-28 |
| 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 |