Bestie: Bayesian Estimation of Intervention Effects

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary 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 (up to around 20 variables) and Monte Carlo schemes (for larger networks) are implemented.

Version: 0.1.1
Imports: BiDAG (≥ 1.3.0), Rcpp (≥ 1.0.3)
LinkingTo: Rcpp
Published: 2020-03-30
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

Downloads:

Reference manual: Bestie.pdf
Package source: Bestie_0.1.1.tar.gz
Windows binaries: r-prerelease: Bestie_0.1.1.zip, r-release: Bestie_0.1.1.zip, r-oldrel: Bestie_0.1.1.zip
macOS binaries: r-prerelease: Bestie_0.1.1.tgz, r-release: Bestie_0.1.1.tgz, r-oldrel: not available

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