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micd (version 1.1.2)

Multiple Imputation in Causal Graph Discovery

Description

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) ; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) ; Witte J, Foraita R, Didelez V (2022) .

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Install

install.packages('micd')

Monthly Downloads

231

Version

1.1.2

License

GPL (>= 3)

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Repository

https://github.com/bips-hb/micd

Maintainer

Ronja Foraita

Last Published

October 23rd, 2025

Functions in micd (1.1.2)

Generate residuals based on variables in imputed data sets

make.formulas.saturated

Creates a formulas Argument

Wrapper for gaussCItest, disCItest and mixCItest

Test Conditional Independence of Gaussians via Fisher's Z Using Multiple Imputations

Fisher's z-Test for (Conditional) Independence between Gaussian Variables with Missings

G square Test for (Conditional) Independence between Discrete Variables with Missings

G square Test for (Conditional) Independence between Discrete Variables after Multiple Imputation

Wrapper for gaussCItwd, disCItwd and mixCItwd

Estimate a PAG by the FCI-MI Algorithm for Multiple Imputed Data Sets of Continuous Data

Wrapper for gaussMItest, disMItest and mixMItest

Estimate (Initial) Skeleton of a DAG using the PC Algorithm for Multiple Imputed Data Sets of Continuous Data

Likelihood Ratio Test for (Conditional) Independence between Mixed Variables after Multiple Imputation

Obtain 'suffStat' for conditional independence testing

Bootstrap Resampling for the PC-MI- and the FCI-MI-algorithm

Likelihood Ratio Test for (Conditional) Independence between Mixed Variables

Estimate the Equivalence Class of a DAG Using the PC-MI Algorithm for Multiple Imputed Data Sets

Evaluate Causal Graph Discovery Algorithm in Multiple Imputed Data sets

Likelihood Ratio Test for (Conditional) Independence between Mixed Variables with Missings