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Bestie: Bayesian Estimation of Intervention Effects

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.

Version:	0.1.5
Imports:	BiDAG (≥ 2.0.0),Rcpp (≥ 1.0.3),mvtnorm (≥ 1.1.0)
LinkingTo:	Rcpp
Published:	2022-04-28
DOI:	10.32614/CRAN.package.Bestie
Author:	Jack Kuipers [aut,cre] and Giusi Moffa [aut]
Maintainer:	Jack Kuipers <jack.kuipers at bsse.ethz.ch>
License:	GPL-3
NeedsCompilation:	yes
CRAN checks:	Bestie results

Documentation:

Reference manual:

Bestie.html ,Bestie.pdf

Downloads:

Package source:	Bestie_0.1.5.tar.gz
Windows binaries:	r-devel:Bestie_0.1.5.zip, r-release:Bestie_0.1.5.zip, r-oldrel:Bestie_0.1.5.zip
macOS binaries:	r-release (arm64):Bestie_0.1.5.tgz, r-oldrel (arm64):Bestie_0.1.5.tgz, r-release (x86_64):Bestie_0.1.5.tgz, r-oldrel (x86_64):Bestie_0.1.5.tgz
Old sources:	Bestie archive

Linking:

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