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: |
2.7-6 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, graphics, utils, methods, abind, graph, RBGL, igraph, ggm, corpcor, robustbase, vcd, Rcpp, bdsmatrix, sfsmisc (≥
1.0-26), fastICA, clue |
| LinkingTo: |
Rcpp (≥ 0.11.0), RcppArmadillo, BH |
| Suggests: |
MASS, Matrix, Rgraphviz, mvtnorm, huge, ggplot2, dagitty |
| Published: |
2022-04-04 |
| 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],
Marco Eigenmann [ctb],
Leonard Henckel [ctb],
Joris Mooij [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: |
CausalInference, GraphicalModels |
| CRAN checks: |
pcalg results |