Package: mimar 0.8.0

mimar: Compact Multiple Imputation, Assessment, and Reporting

Provides compact tools for missing-data analysis, including artificial amputation, chained single and multiple imputation, statistical and machine-learning-based imputation methods, diagnostic evaluation, and post-imputation pooling.

Authors:Imad EL BADISY [aut, cre]

mimar_0.8.0.tar.gz
mimar_0.8.0.zip(r-4.7)mimar_0.8.0.zip(r-4.6)mimar_0.8.0.zip(r-4.5)
mimar_0.8.0.tgz(r-4.6-any)mimar_0.8.0.tgz(r-4.5-any)
mimar_0.8.0.tar.gz(r-4.7-any)mimar_0.8.0.tar.gz(r-4.6-any)
mimar_0.8.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mimar/json (API)
NEWS

# Install 'mimar' in R:

install.packages('mimar', repos = c('https://ielbadisy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ielbadisy/mimar/issues

On CRAN:

data-analysis imputation machine-learning missing-data multiple-imputation statistics

4.18 score 9 exports 144 dependencies

Last updated from:97a42db307. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	170
source / vignettes	OK	220
linux-release-x86_64	OK	165
macos-release-arm64	OK	140
macos-oldrel-arm64	OK	99
windows-devel	OK	105
windows-release	OK	111
windows-oldrel	OK	93
wasm-release	OK	146

Exports:ampute complete describe evaluate fit impute imputer imputer_registry pool

Dependencies:abind backports BART base64enc bit bit64 boot broom bslib cachem car carData class cli clipr cluster codetools colorspace cowplot cpp11 crayon crosstalk data.table Deriv digest doBy doParallel dplyr DT e1071 ellipse emmeans estimability evaluate FactoMineR farver fastmap flashClust fontawesome forcats foreach forecast Formula fracdiff fs functionals gbm generics ggplot2 ggrepel glmnet glue gtable haven highr hms htmltools htmlwidgets isoband iterators jomo jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels memoise mgcv mice microbenchmark mime minqa missMDA mitml modelr multcompView mvtnorm naivebayes nlme nloptr nnet numDeriv ordinal otel pan pbkrtest pillar pkgconfig prettyunits progress promises proxy purrr quantreg R6 ranger rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr reformulas rlang rmarkdown rpart S7 sass scales scatterplot3d shape SparseM stringi stringr survival tibble tidyr tidyselect timeDate tinytex tzdb ucminf urca utf8 vctrs viridisLite vroom withr xfun xgboost yaml zoo

Chained Multiple Imputation Workflows with mimar

Rendered frommimar.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-02
Started: 2026-05-14

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Artificially introduce missing data	ampute ampute.data.frame
Extract completed imputed data	complete
Describe missing data and mimar objects	describe
Evaluate imputation quality	evaluate
Fit an imputer learner	fit fit.mimar_imputer
Impute missing data	impute impute.data.frame impute.mimar_amputation
Build an imputer learner	imputer imputer.default
List available mimar imputers	imputer_registry
Diagnostic plots for mimar imputations	plot.mimar_imputation
Pool post-fit quantities across imputations	pool pool.data.frame pool.list pool.matrix pool.numeric