Package: cia 1.1.0

Mathew Varidel
cia: Learn and Apply Directed Acyclic Graphs for Causal Inference
Causal Inference Assistance (CIA) for performing causal inference within the structural causal modelling framework. Structure learning is performed using partition Markov chain Monte Carlo (Kuipers & Moffa, 2017) and several additional functions have been added to help with causal inference. Kuipers and Moffa (2017) <doi:10.1080/01621459.2015.1133426>.
Authors:
cia_1.1.0.tar.gz
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cia_1.1.0.tgz(r-4.5-any)cia_1.1.0.tgz(r-4.4-any)
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cia_1.1.0.tgz(r-4.4-emscripten)
cia.pdf |cia.html✨
cia/json (API)
NEWS
# Install 'cia' in R: |
install.packages('cia', repos = c('https://spaceodyssey.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/spaceodyssey/cia/issues
Pkgdown site:https://spaceodyssey.github.io
Last updated 3 months agofrom:39589e1a09. Checks:6 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Feb 17 2025 |
R-4.5-win | OK | Feb 17 2025 |
R-4.5-mac | OK | Feb 17 2025 |
R-4.5-linux | OK | Feb 17 2025 |
R-4.4-win | OK | Feb 17 2025 |
R-4.4-mac | OK | Feb 17 2025 |
Exports:BNLearnScorerCalculateAcceptanceRatesCalculateEdgeProbabilitiesCalculateFeatureMeanCollectUniqueObjectsCoupledPartitionMCMCCreateScorerDAGtoCPDAGDAGtoPartitionDefaultProposalFlattenChainsGetEmptyDAGGetIncrementalScoringEdgesGetLowestPairwiseScoringEdgesGetMAPInitCoupledPartitionInitPartitionMutilateGraphPartitionMCMCPartitiontoDAGPlotConcordancePlotCumulativeMeanTracePlotScoreTracePostProcessChainsSampleChainsSampleEdgeProbabilitiesSamplePosteriorPredictiveChainsScoreDAGScoreLabelledPartitiontoBNLearntogRaintoMatrixUniformlySampleDAG
Dependencies:arrangementsbackportsbnlearnbroomclicodetoolscolorspacecpp11doParalleldplyrfansifarverfastmatchforeachgenericsggplot2gluegmpgRaingRbasegtableigraphisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepatchworkpillarpkgconfigpurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr