recipe r-pcalg

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.

Homepage

http://pcalg.r-forge.r-project.org/

License

GPL2 / GPL-2

Recipe

/r-pcalg/meta.yaml

package r-pcalg

Versions

2.6_12-3,  2.6_12-2,  2.6_12-1,  2.6_12-0

Depends

• bioconductor-graph

• bioconductor-rbgl

• libgcc-ng >=10.3.0

• libstdcxx-ng >=10.3.0

• r-abind

• r-base >=4.1,<4.2.0a0

• r-bdsmatrix

• r-bh

• r-clue

• r-corpcor

• r-dagitty

• r-fastica

• r-ggm

• r-igraph

• r-rcpp

• r-rcpparmadillo

• r-robustbase

• r-sfsmisc >=1.0_26

• r-vcd

Required By

• bioconductor-mirlab

• bioconductor-dce

• bioconductor-epinem

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install r-pcalg

and update with:

conda update r-pcalg

or use the docker container:

docker pull quay.io/biocontainers/r-pcalg:<tag>

(see r-pcalg/tags for valid values for <tag>)

Download stats

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/r-pcalg/README.html)