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:Mathew Varidel [aut, cre, cph], Victor An [ctb]

cia_1.1.0.tar.gz
cia_1.1.0.zip(r-4.7)cia_1.1.0.zip(r-4.6)cia_1.1.0.zip(r-4.5)
cia_1.1.0.tgz(r-4.6-any)cia_1.1.0.tgz(r-4.5-any)
cia_1.1.0.tar.gz(r-4.7-any)cia_1.1.0.tar.gz(r-4.6-any)
cia_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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/docs site:https://spaceodyssey.github.io

On CRAN:

Conda:

2.70 score 5 scripts 474 downloads 33 exports 48 dependencies

Last updated from:39589e1a09. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK194
source / vignettesOK211
linux-release-x86_64OK176
macos-release-arm64OK214
macos-oldrel-arm64OK209
windows-develOK135
windows-releaseOK123
windows-oldrelOK139
wasm-releaseOK130

Exports:BNLearnScorerCalculateAcceptanceRatesCalculateEdgeProbabilitiesCalculateFeatureMeanCollectUniqueObjectsCoupledPartitionMCMCCreateScorerDAGtoCPDAGDAGtoPartitionDefaultProposalFlattenChainsGetEmptyDAGGetIncrementalScoringEdgesGetLowestPairwiseScoringEdgesGetMAPInitCoupledPartitionInitPartitionMutilateGraphPartitionMCMCPartitiontoDAGPlotConcordancePlotCumulativeMeanTracePlotScoreTracePostProcessChainsSampleChainsSampleEdgeProbabilitiesSamplePosteriorPredictiveChainsScoreDAGScoreLabelledPartitiontoBNLearntogRaintoMatrixUniformlySampleDAG

Dependencies:arrangementsbackportsbnlearnbroomclicodetoolscpp11doParalleldplyrfarverfastmatchforeachgenericsggplot2gluegmpgRaingRbasegtableigraphisobanditeratorslabelinglatticelifecyclemagrittrMatrixpatchworkpillarpkgconfigpurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Index a cia_chain object[.cia_chain
Index a cia_chains object[.cia_chains
Indexing with respect to iterations.[.cia_post_chain
Index a cia_post_chains object with respect to iterations.[.cia_post_chains
Index a cia_chains object[[.cia_chains
Index a cia_post_chains object.[[.cia_post_chains
BNLearnScorerBNLearnScorer
Calculate acceptance ratesCalculateAcceptanceRates
Calculate pairwise edge probabilitiesCalculateEdgeProbabilities
Calculate arithmetic mean for a DAG featureCalculateFeatureMean
Collect unique objectsCollectUniqueObjects
Coupled Partition MCMCCoupledPartitionMCMC
Scorer constructorCreateScorer
Convert DAG to CPDAGDAGtoCPDAG
Convert DAG to partitionDAGtoPartition
Default proposal constructorDefaultProposal
Flatten chainsFlattenChains
Get an empty DAG given a set of nodes.GetEmptyDAG
Get incremental edgesGetIncrementalScoringEdges
Preprocessing for blacklisting Get the lowest pairwise scoring edges.GetLowestPairwiseScoringEdges
Get the maximum a posteriori stateGetMAP
Initialise partition state for SampleChains. *[Experimental]*InitCoupledPartition
Initialise states for SampleChains. Initialise partition state for SampleChains.InitPartition
Mutilate graphMutilateGraph
Transition objects. Partition MCMCPartitionMCMC
Sample DAG from partitionPartitiontoDAG
Concordance plotPlotConcordance
Plot cumulative mean trace plot.PlotCumulativeMeanTrace
Plot the score tracePlotScoreTrace
Index chains for further analysis *[Deprecated]*PostProcessChains
Sample chainsSampleChains
Sample edge probabilitiesSampleEdgeProbabilities
Draw from a posterior predictive distributionSamplePosteriorPredictiveChains
Score DAG.ScoreDAG
Score labelled partitionScoreLabelledPartition
Convert to bnlearn object.toBNLearn
Convert to a gRain object.togRain
Convert to adjacency matrix.toMatrix
Uniformly sample DAGUniformlySampleDAG