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:
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)
| # Install 'Bestie' in R: |
| install.packages('Bestie', repos = c('https://jackkuipers.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:1c4f01c0ed. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 07 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 07 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 07 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 07 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 07 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 07 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 07 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 07 2024 |
Exports:DAGinterventionDAGinterventionMCDAGparameters
Dependencies:abindbdsmatrixBHBiDAGBiocGenericsBiocManagercliclueclustercodacolorspacecorpcorcpp11DEoptimRfastICAgenericsggmgluegraphigraphlatticelifecyclelmtestmagrittrMASSMatrixmvtnormpcalgpkgconfigRBGLRcppRcppArmadilloRgraphvizrlangrobustbasesfsmiscvcdvctrszoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Exact estimation of intervention effects for a single DAG or a chain of sampled DAGs | DAGintervention |
| Monte Carlo estimation of intervention effects for a DAG or chain of sampled DAGs | DAGinterventionMC |
| Augment a DAG with parameters | DAGparameters |
