The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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.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:

Please use the canonical form https://CRAN.R-project.org/package=Bestie to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.