Package: cia 1.0.0.9000

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]

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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

On CRAN:

3.81 score 5 scripts 30 downloads 30 exports 53 dependencies

Last updated 5 days agofrom:0d2ba0af3a. Checks:OK: 5. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 18 2024
R-4.5-win	OK	Nov 18 2024
R-4.5-linux	OK	Nov 18 2024
R-4.4-win	OK	Nov 18 2024
R-4.4-mac	OK	Nov 18 2024

Exports:BNLearnScorer CalculateAcceptanceRates CalculateEdgeProbabilities CalculateFeatureMean CollectUniqueObjects CreateScorer DAGtoCPDAG DAGtoPartition DefaultProposal FlattenChains GetEmptyDAG GetIncrementalScoringEdges GetLowestPairwiseScoringEdges GetMAP MutilateGraph PartitionMCMC PartitiontoDAG PlotConcordance PlotCumulativeMeanTrace PlotScoreTrace PostProcessChains SampleChains SampleEdgeProbabilities SamplePosteriorPredictiveChains ScoreDAG ScoreLabelledPartition toBNLearn togRain toMatrix UniformlySampleDAG

Dependencies:arrangements backports bnlearn broom cli codetools colorspace cpp11 doParallel dplyr fansi farver fastmatch foreach generics ggplot2 glue gmp gRain gRbase gtable igraph isoband iterators labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme patchwork pillar pkgconfig purrr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
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
BNLearnScorer	BNLearnScorer
Calculate acceptance rates	CalculateAcceptanceRates
Calculate pairwise edge probabilities	CalculateEdgeProbabilities
Calculate arithmetic mean for a DAG feature	CalculateFeatureMean
Collect unique objects	CollectUniqueObjects
Scorer constructor	CreateScorer
Convert DAG to CPDAG	DAGtoCPDAG
Convert DAG to partition	DAGtoPartition
Default proposal constructor	DefaultProposal
Flatten chains	FlattenChains
Get an empty DAG given a set of nodes.	GetEmptyDAG
Get incremental edges	GetIncrementalScoringEdges
Preprocessing for blacklisting Get the lowest pairwise scoring edges.	GetLowestPairwiseScoringEdges
Get the maximum a posteriori state	GetMAP
Mutilate graph	MutilateGraph
Transition objects. Partition MCMC	PartitionMCMC
Sample DAG from partition	PartitiontoDAG
Concordance plot	PlotConcordance
Plot cumulative mean trace plot.	PlotCumulativeMeanTrace
Plot the score trace	PlotScoreTrace
Index chains for further analysis	PostProcessChains
Sample chains	SampleChains
Sample edge probabilities	SampleEdgeProbabilities
Draw from a posterior predictive distribution	SamplePosteriorPredictiveChains
Score DAG.	ScoreDAG
Score labelled partition	ScoreLabelledPartition
Convert to bnlearn object.	toBNLearn
Convert to a gRain object.	togRain
Convert to adjacency matrix.	toMatrix
Uniformly sample DAG	UniformlySampleDAG