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miceadds

Some Additional Multiple Imputation Functions, Especially for 'mice'

If you use miceadds and have suggestions for improvement or have found bugs, please email me at robitzsch@leibniz-ipn.de. Please always provide a minimal dataset, necessary to demonstrate the problem, a minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and all necessary information on the used librarys, the R version, and the OS it is run on, perhaps a sessionInfo().

Manual

The manual can be found here https://alexanderrobitzsch.github.io/miceadds/

CRAN version

The official version of miceadds is hosted on CRAN and can be found here. The CRAN version can be installed from within R using:

utils::install.packages("miceadds")

GitHub version

The version hosted here is the development version of miceadds. The GitHub version can be installed from within R using:

devtools::install_github("alexanderrobitzsch/miceadds")

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Version

Install

install.packages('miceadds')

Monthly Downloads

8,639

Version

3.18-36

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Repository

https://github.com/alexanderrobitzsch/miceadds

Homepage

https://sites.google.com/view/alexander-robitzsch/software

Maintainer

Alexander Robitzsch

Last Published

September 12th, 2025

Functions in miceadds (3.18-36)

data.smallscale

Small-Scale Dataset for Testing Purposes (Moderate Number of Cases, Many Variables)

Example Datasets for miceadds Package

Datasets from Allison's Missing Data Book

Converting a List of Multiply Imputed Data Sets into a mids Object

Converting an Object of class amelia

Datasets from Enders' Missing Data Book

Dataset Internet

Datasets from Grahams Missing Data Book

cxxfunction.copy

R Utilities: Copy of an Rcpp File

data.largescale

Large-scale Dataset for Testing Purposes (Many Cases, Few Variables)

Indicator Function for Analyzing Coverage

Converts a jomo Data Frame in Long Format into a List of Datasets or an Object of Class mids

index.dataframe

R Utilities: Include an Index to a Data Frame

Plausible Value Imputation Using a Known Measurement Error Variance (Based on Classical Test Theory)

Creates Objects of Class datlist or nested.datlist

R Utilities: Vector Based Versions of grep

Simulating Univariate Data from Fleishman Power Normal Transformations

Some Functionality for Strings and File Names

Moves Files from One Directory to Another Directory

Kernel PLS Regression

R Utilities: Loading Rdata Files in a Convenient Way

Statistical Inference for Fixed and Random Structure for Fitted Models in lme4

library_install

R Utilities: Loading a Package or Installation of a Package if Necessary

Standardization of a Matrix

R Utilities: Loading/Reading Data Files using miceadds

Cluster Robust Standard Errors for Linear Models and General Linear Models

Analysis of Variance for Multiply Imputed Data Sets (Using the \(D_2\) Statistic)

Simulating Normally Distributed Data

ma_lme4_formula

Utility Functions for Working with lme4 Formula Objects

Some Multivariate Descriptive Statistics for Weighted Data in miceadds

mice.impute.catpmm

Imputation of a Categorical Variable Using Multivariate Predictive Mean Matching

mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables

Cohen's d Effect Size for Missingness Indicators

mice.impute.hotDeck

Imputation of a Variable Using Probabilistic Hot Deck Imputation

mice.impute.2lonly.function

Imputation at Level 2 (in miceadds)

mice.impute.2l.latentgroupmean.ml

Imputation of Latent and Manifest Group Means for Multilevel Data

mice.impute.constant

Imputation Using a Fixed Vector

Multiple Imputation by Chained Equations using One Chain

mice.impute.imputeR.lmFun

Wrapper Function to Imputation Methods in the imputeR Package

mice.impute.bygroup

Groupwise Imputation Function

mice.impute.smcfcs

Substantive Model Compatible Multiple Imputation (Single Level)

mice.impute.pls

Imputation using Partial Least Squares for Dimension Reduction

mice.impute.weighted.pmm

Imputation by Weighted Predictive Mean Matching or Weighted Normal Linear Regression

mice.impute.pmm3

Imputation by Predictive Mean Matching (in miceadds)

mice.impute.tricube.pmm

Imputation by Tricube Predictive Mean Matching

mice.impute.synthpop

Using a synthpop Synthesizing Method in the mice Package

mice.impute.rlm

Imputation of a Linear Model by Bayesian Bootstrap

mice.impute.simputation

Wrapper Function to Imputation Methods in the simputation Package

Nested Multiple Imputation

mice.impute.ml.lmer

Multilevel Imputation Using lme4

Combination of F Statistics for Multiply Imputed Datasets Using a Chi Square Approximation

mice.impute.plausible.values

Plausible Value Imputation using Classical Test Theory and Based on Individual Likelihood

miceadds-package

tools:::Rd_package_title("miceadds")

micombine.chisquare

Combination of Chi Square Statistics of Multiply Imputed Datasets

miceadds-utilities

Utility Functions in miceadds

Arguments for mice::mice Function

mice_imputation_2l_lmer

Imputation of a Continuous or a Binary Variable From a Two-Level Regression Model using lme4 or blme

miceadds-defunct

Defunct miceadds Functions

Converting a mids, mids.1chain or mids.nmi Object in a Dataset List

Inference for Correlations and Covariances for Multiply Imputed Datasets

R Utilities: Rounding DIN 1333 (Kaufmaennisches Runden)

Pooling for Nested Multiple Imputation

Simulation of Multivariate Linearly Related Non-Normal Variables

nestedList2List

Converting a Nested List into a List (and Vice Versa)

R Utilities: Formatting R Output on the R Console

R Utilities: Save a Data Frame in Rdata Format

Principal Component Analysis with Ridge Regularization

Statistical Inference for Multiply Imputed Datasets

str_C.expand.grid

R Utilities: String Paste Combined with expand.grid

Synthesizing Method for Fixed Values by Design in synthpop

Descriptive Statistics for a Vector or a Data Frame

Synthesizing Method for synthpop Using a Formula Interface

R Utilities: Saving/Writing Data Files using miceadds

Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets

sumpreserving.rounding

Sum Preserving Rounding

Subsetting Multiply Imputed Datasets and Nested Multiply Imputed Datasets

Export mids object to MLwiN

R Utilities: Source all R or Rcpp Files within a Directory

R Utilities: Scan a Character Vector

Constructs Synthetic Dataset with mice Imputation Methods

Reading and Writing Files in Fixed Width Format

Evaluates an Expression for (Nested) Multiply Imputed Datasets

MCMC Estimation for Mixed Effects Model

Two-Way Imputation

visitSequence.determine

Automatic Determination of a Visit Sequence in mice

Write a List of Multiply Imputed Datasets

write.mice.imputation

Export Multiply Imputed Datasets from a mids Object

Using a mice Imputation Method in the synthpop Package

Writing a Data Frame into SPSS Format Using PSPP Software

Generation of Synthetic Data Utilizing Data Augmentation

R Utilities: Various Strings Representing System Time

R Utilities: Evaluates a String as an Expression in R

R Utilities: R Session Information

R Utilities: Removing CF Line Endings

NestedImputationList

Functions for Analysis of Nested Multiply Imputed Datasets

Wald Test for Nested Multiply Imputed Datasets

complete.miceadds

Creates Imputed Dataset from a mids.nmi or mids.1chain Object

VariableNames2String

Stringing Variable Names with Line Breaks

Calculation of Groupwise Descriptive Statistics for Matrices

Rhat Convergence Statistic of a mice Imputation

Rfunction_include_argument_values

Utility Functions for Writing R Functions