MCMC diagnostics of convergence: function get_diagnostics() | Posterior inference: DAG structure learning | Posterior inference: causal effect estimation | References
Package: BCDAG 1.1.4
BCDAG: Bayesian Structure and Causal Learning of Gaussian Directed Graphs
A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>, F. Castelletti and A. Mascaro (2026) <doi:10.18637/jss.v116.i05>.
Authors:
BCDAG_1.1.4.tar.gz
BCDAG_1.1.4.zip(r-4.7)BCDAG_1.1.4.zip(r-4.6)BCDAG_1.1.4.zip(r-4.5)
BCDAG_1.1.4.tgz(r-4.6-any)BCDAG_1.1.4.tgz(r-4.5-any)
BCDAG_1.1.4.tar.gz(r-4.7-any)BCDAG_1.1.4.tar.gz(r-4.6-any)
BCDAG_1.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BCDAG/json (API)
| # Install 'BCDAG' in R: |
| install.packages('BCDAG', repos = c('https://alesmascaro.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/alesmascaro/bcdag/issues
Datasets:
- leukemia - Protein levels for 68 diagnosed AML patients of subtype M2
Last updated from:8280d0cc5d. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 184 | ||
| source / vignettes | OK | 303 | ||
| linux-release-x86_64 | OK | 143 | ||
| macos-release-arm64 | OK | 82 | ||
| macos-oldrel-arm64 | OK | 134 | ||
| windows-devel | OK | 94 | ||
| windows-release | OK | 99 | ||
| windows-oldrel | OK | 84 | ||
| wasm-release | OK | 117 |
Exports:as_graphNELcausaleffectget_causaleffectget_diagnosticsget_edgeprobsget_MAPdagget_MPMdagget_neighboringDAGslearn_DAGrDAGrDAGWishart
Dependencies:BiocGenericsclicpp11genericsgluegraphgRbaseigraphlatticelifecyclemagrittrMatrixmvtnormpkgconfigRcppRcppArmadilloRcppEigenRgraphvizrlangvctrs
Elaborate on the output of learn_DAG() using get_ functions
Last update: 2025-02-27
Started: 2022-01-20
MCMC scheme for posterior inference of Gaussian DAG models: the learn_DAG() function
Model description | Generating data | learn_DAG() | Input | Output | A note on fast = TRUE | References
Last update: 2024-02-09
Started: 2022-01-20
Random data generation from Gaussian DAG models
Generating DAGs and parameters: functions rDAG() and rDAGWishart() | Generating Gaussian DAG parameters | Generating data from a Gaussian DAG model | References
Last update: 2024-01-01
Started: 2021-12-27
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Transform adjacency matrix into graphNEL object | as_graphNEL |
| Compute causal effects between variables | causaleffect |
| Credible Intervals for bcdagCE Object | confint.bcdagCE |
| Estimate total causal effects from the MCMC output | get_causaleffect |
| MCMC diagnostics | get_diagnostics |
| Compute posterior probabilities of edge inclusion from the MCMC output | get_edgeprobs |
| Compute the maximum a posteriori DAG model from the MCMC output | get_MAPdag |
| Compute the median probability DAG model from the MCMC output | get_MPMdag |
| Enumerate all neighbors of a DAG | get_neighboringDAGs |
| MCMC scheme for Gaussian DAG posterior inference | learn_DAG |
| Protein levels for 68 diagnosed AML patients of subtype M2 | leukemia |
| bcdag object plot | plot.bcdag |
| bcdagCE object plot | plot.bcdagCE |
| bcdag object print | print.bcdag |
| bcdagCE object print | print.bcdagCE |
| Generate a Directed Acyclic Graph (DAG) randomly | rDAG |
| Random samples from a compatible DAG-Wishart distribution | rDAGWishart |
| bcdag object summaries | summary.bcdag |
| bcdagCE object summary | summary.bcdagCE |
