Package: Bestie 0.1.5

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.

Authors:Jack Kuipers [aut,cre] and Giusi Moffa [aut]

Bestie_0.1.5.tar.gz
Bestie_0.1.5.zip(r-4.5)Bestie_0.1.5.zip(r-4.4)Bestie_0.1.5.zip(r-4.3)
Bestie_0.1.5.tgz(r-4.4-x86_64)Bestie_0.1.5.tgz(r-4.4-arm64)Bestie_0.1.5.tgz(r-4.3-x86_64)Bestie_0.1.5.tgz(r-4.3-arm64)
Bestie_0.1.5.tar.gz(r-4.5-noble)Bestie_0.1.5.tar.gz(r-4.4-noble)
Bestie_0.1.5.tgz(r-4.4-emscripten)Bestie_0.1.5.tgz(r-4.3-emscripten)
Bestie.pdf |Bestie.html✨
Bestie/json (API)

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3 exports 0.00 score 38 dependencies 3 scripts 249 downloads

Last updated 2 years agofrom:1c4f01c0ed. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:DAGinterventionDAGinterventionMCDAGparameters

Dependencies:abindbdsmatrixBHBiDAGBiocGenericsBiocManagercliclueclustercodacolorspacecorpcorcpp11DEoptimRfastICAggmgluegraphigraphlatticelifecyclelmtestmagrittrMASSMatrixmvtnormpcalgpkgconfigRBGLRcppRcppArmadilloRgraphvizrlangrobustbasesfsmiscvcdvctrszoo