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
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Bestie.pdf |Bestie.html
Bestie/json (API)

# Install 'Bestie' in R:
install.packages('Bestie', repos = c('https://jackkuipers.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

cpp

1.00 score 3 scripts 311 downloads 3 exports 39 dependencies

Last updated 3 years agofrom:1c4f01c0ed. Checks:1 OK, 8 NOTE, 3 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 07 2025
R-4.5-win-x86_64NOTEMar 07 2025
R-4.5-mac-x86_64NOTEMar 07 2025
R-4.5-mac-aarch64NOTEMar 07 2025
R-4.5-linux-x86_64NOTEMar 07 2025
R-4.4-win-x86_64NOTEMar 07 2025
R-4.4-mac-x86_64NOTEMar 07 2025
R-4.4-mac-aarch64NOTEMar 07 2025
R-4.4-linux-x86_64NOTEMar 07 2025
R-4.3-win-x86_64ERRORMar 07 2025
R-4.3-mac-x86_64ERRORMar 07 2025
R-4.3-mac-aarch64ERRORMar 07 2025

Exports:DAGinterventionDAGinterventionMCDAGparameters

Dependencies:abindbdsmatrixBHBiDAGBiocGenericsBiocManagercliclueclustercodacolorspacecorpcorcpp11DEoptimRfastICAgenericsggmgluegraphigraphlatticelifecyclelmtestmagrittrMASSMatrixmvtnormpcalgpkgconfigRBGLRcppRcppArmadilloRgraphvizrlangrobustbasesfsmiscvcdvctrszoo