Package: micd 1.1.2

Ronja Foraita

micd: Multiple Imputation in Causal Graph Discovery

Modified functions of the package 'pcalg' and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.

Authors:Ronja Foraita [aut, cph, cre], Janine Witte [aut]

micd_1.1.2.tar.gz
micd_1.1.2.zip(r-4.7-any)micd_1.1.2.zip(r-4.6-any)micd_1.1.2.zip(r-4.5-any)
micd_1.1.2.tgz(r-4.6-any)micd_1.1.2.tgz(r-4.5-any)
micd_1.1.2.tar.gz(r-4.7-any)micd_1.1.2.tar.gz(r-4.6-any)
micd_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
micd/json (API)

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

Bug tracker:https://github.com/bips-hb/micd/issues

On CRAN:

Conda:

causal-discoverygraphical-modelsmultiple-imputation

3.84 score 6 stars 23 scripts 326 downloads 19 exports 87 dependencies

Last updated from:e686c62db5. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE199
source / vignettesOK273
linux-releaseNOTE206
macos-releaseNOTE193
macos-oldrelNOTE157
windows-develNOTE172
windows-releaseNOTE156
windows-oldrelNOTE180
wasm-releaseOK153

Exports:boot.graphdisCItwddisMItestfciMIflexCItestflexCItwdflexMItestgaussCItestMIgaussCItwdgaussMItestgetSuffmake.formulas.saturatedmakeResidualsmixCItestmixCItwdmixMItestpcMIskeletonMIwith_graph

Dependencies:abindbackportsbdsmatrixBHBiocGenericsBiocManagerbitbit64bootbroomclicliprclueclustercodetoolscolorspacecorpcorcpp11crayonDEoptimRdplyrfastICAforcatsforeachgenericsggmglmnetgluegraphhavenhmsigraphiteratorsjomolatticelifecyclelme4lmtestmagrittrMASSMatrixmiceminqamitmlnlmenloptrnnetnumDerivordinalpanpcalgpillarpkgconfigprettyunitsprogresspurrrR6RBGLrbibutilsRcppRcppArmadilloRcppEigenRcppParallelRdpackreadrreformulasRfastrlangrobustbaserpartsfsmiscshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vcdvctrsvroomwithrziggzoo

Readme and manuals

Help Manual

Help pageTopics
Bootstrap Resampling for the PC-MI- and the FCI-MI-algorithmboot.graph
G square Test for (Conditional) Independence between Discrete Variables with MissingsdisCItwd
G square Test for (Conditional) Independence between Discrete Variables after Multiple ImputationdisMItest
Estimate a PAG by the FCI-MI Algorithm for Multiple Imputed Data Sets of Continuous DatafciMI
Wrapper for gaussCItest, disCItest and mixCItestflexCItest
Wrapper for gaussCItwd, disCItwd and mixCItwdflexCItwd
Wrapper for gaussMItest, disMItest and mixMItestflexMItest
Fisher's z-Test for (Conditional) Independence between Gaussian Variables with MissingsgaussCItwd
Test Conditional Independence of Gaussians via Fisher's Z Using Multiple ImputationsgaussCItestMI gaussMItest
Obtain 'suffStat' for conditional independence testinggetSuff
Creates a 'formulas' Argumentmake.formulas.saturated
Generate residuals based on variables in imputed data setsmakeResiduals
Likelihood Ratio Test for (Conditional) Independence between Mixed VariablesmixCItest
Likelihood Ratio Test for (Conditional) Independence between Mixed Variables with MissingsmixCItwd
Likelihood Ratio Test for (Conditional) Independence between Mixed Variables after Multiple ImputationmixMItest
Estimate the Equivalence Class of a DAG Using the PC-MI Algorithm for Multiple Imputed Data SetspcMI
Estimate (Initial) Skeleton of a DAG using the PC Algorithm for Multiple Imputed Data Sets of Continuous DataskeletonMI
Evaluate Causal Graph Discovery Algorithm in Multiple Imputed Data setswith_graph