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 |
| Reference manual: | Bestie.html , Bestie.pdf |
| 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 |
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