Package: causalBatch 1.4.0

Eric W. Bridgeford

causalBatch: Causal Batch Effects

Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <doi:10.48550/arXiv.2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.

Authors:Eric W. Bridgeford [aut, cre], Michael Powell [ctb], Brian Caffo [ctb], Joshua T. Vogelstein [ctb]

causalBatch_1.4.0.tar.gz
causalBatch_1.4.0.zip(r-4.5)causalBatch_1.4.0.zip(r-4.4)causalBatch_1.4.0.zip(r-4.3)
causalBatch_1.4.0.tgz(r-4.5-any)causalBatch_1.4.0.tgz(r-4.4-any)causalBatch_1.4.0.tgz(r-4.3-any)
causalBatch_1.4.0.tar.gz(r-4.5-noble)causalBatch_1.4.0.tar.gz(r-4.4-noble)
causalBatch_1.4.0.tgz(r-4.4-emscripten)causalBatch_1.4.0.tgz(r-4.3-emscripten)
causalBatch.pdf |causalBatch.html
causalBatch/json (API)

# Install 'causalBatch' in R:
install.packages('causalBatch', repos = c('https://neurodata.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/neurodata/causal_batch/issues

On CRAN:

Conda:

4.70 score 4 stars 23 scripts 249 downloads 15 exports 93 dependencies

Last updated 14 days agofrom:b73e581699. Checks:9 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesFAILMar 20 2025
R-4.5-winERRORMar 20 2025
R-4.5-macERRORMar 20 2025
R-4.5-linuxERRORMar 20 2025
R-4.4-winERRORMar 20 2025
R-4.4-macERRORMar 20 2025
R-4.4-linuxERRORMar 20 2025
R-4.3-winERRORMar 20 2025
R-4.3-macERRORMar 20 2025

Exports:cb.align.kway_matchcb.align.vm_trimcb.correct.aipw_cComBatcb.correct.apply_aipw_cComBatcb.correct.apply_cComBatcb.correct.matching_cComBatcb.detect.caus_cdcorrcb.sims.cate.heteroskedastic_simcb.sims.cate.kclass_sigmoidal_simcb.sims.cate.nonmonotone_simcb.sims.cate.sigmoidal_simcb.sims.sim_impulsecb.sims.sim_impulse_asycovcb.sims.sim_linearcb.sims.sim_sigmoid

Dependencies:annotateAnnotationDbiaskpassbackportsBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbootcachemcdcsischkclicodetoolscpp11crayoncubaturecurlDBIdplyredgeRfansifastmapFNNformatRfutile.loggerfutile.optionsgenefiltergenericsGenomeInfoDbGenomeInfoDbDatagluehttrIRangesjsonliteKEGGRESTkernlabKernSmoothkslambda.rlatticelifecyclelimmalocfitmagrittrMASSMatchItMatrixMatrixGenericsMatrixModelsmatrixStatsmclustmemoisemgcvmimemulticoolmvtnormnlmennetnpopensslpillarpkgconfigplogrpngpracmaquadprogquantregR6RcppRcppProgressrlangRSQLiteS4VectorssnowSparseMstatmodsurvivalsvasystibbletidyselectUCSC.utilsutf8vctrswithrXMLxtableXVector

Readme and manuals

Help Manual

Help pageTopics
K-Way matchingcb.align.kway_match
Vector Matchingcb.align.vm_trim
Augmented Inverse Probability Weighting Conditional ComBatcb.correct.aipw_cComBat
Fit AIPW ComBat model for batch effect correctioncb.correct.apply_aipw_cComBat
Adjust for batch effects using an empirical Bayes frameworkcb.correct.apply_cComBat
Matching Conditional ComBatcb.correct.matching_cComBat
Causal Conditional Distance Correlationcb.detect.caus_cdcorr
Simulate Data with Heteroskedastic Conditional Average Treatment Effectscb.sims.cate.heteroskedastic_sim
K-class Sigmoidal CATE Simulationcb.sims.cate.kclass_sigmoidal_sim
Non-monotone CATE Simulationcb.sims.cate.nonmonotone_sim
Sigmoidal CATE Simulationcb.sims.cate.sigmoidal_sim
Impulse Simulationcb.sims.sim_impulse
Impulse Simulation with Asymmetric Covariatescb.sims.sim_impulse_asycov
Linear Simulationcb.sims.sim_linear
Sigmoidal Simulationcb.sims.sim_sigmoid