Package: mimar 1.4.0

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_1.4.0.tar.gz
mimar_1.4.0.zip(r-4.7-any)mimar_1.4.0.zip(r-4.6-any)mimar_1.4.0.zip(r-4.5-any)
mimar_1.4.0.tgz(r-4.6-any)mimar_1.4.0.tgz(r-4.5-any)
mimar_1.4.0.tar.gz(r-4.7-any)mimar_1.4.0.tar.gz(r-4.6-any)
mimar_1.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mimar/json (API)

# 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

4.60 score 1 stars 241 downloads 19 exports 151 dependencies

Last updated from:e6fc8c2e30. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	203
source / vignettes	OK	265
linux-release	NOTE	160
macos-release	NOTE	136
macos-oldrel	NOTE	111
windows-devel	NOTE	121
windows-release	NOTE	111
windows-oldrel	NOTE	129
wasm-release	OK	164

Exports:ampute complete describe evaluate fit impute imputer imputer_registry ipcw nelsonaalen pool pool_clogit pool_coxph pool_glm pool_lm pool_panglm pool_survmat pool_survreg pool_test

Dependencies:abind backports BART base64enc basetable bit bit64 boot broom bslib cachem car carData class cli clipr cluster codetools colorspace cowplot cpp11 crayon crosstalk data.table densemlp Deriv digest doBy doParallel dplyr DT e1071 ellipse emmeans estimability evaluate FactoMineR farver fastgbm fastmap flashClust fontawesome forcats foreach forecast Formula fracdiff fs functionals generics ggplot2 ggrepel glmnet glue gtable haven highr hms htmltools htmlwidgets irlba isoband iterators jomo jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels memoise mgcv mice mime minqa missknn 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 RcppParallel Rdpack readr reformulas rlang rmarkdown rpart S7 sass scales scatterplot3d shape showtext showtextdb SparseM stringi stringr survival sysfonts 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::rmarkdown

Last update: 2026-09-14
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
Inverse probability of censoring weights	ipcw
Nelson-Aalen cumulative hazard as an imputation predictor	nelsonaalen
Diagnostic plots for mimar imputations	plot.mimar_imputation
Pool post-fit quantities across imputations	pool pool.data.frame pool.list pool.matrix pool.numeric
Pool conditional logistic regression models across imputations	pool_clogit
Pool Cox proportional hazards models across imputations	pool_coxph
Pool generalized linear models across imputations	pool_glm
Pool linear models across imputations	pool_lm
Pool panel (panglm) models across imputations	pool_panglm
Pool survival-probability matrices across imputations	pool_survmat
Pool parametric survival regression models across imputations	pool_survreg
Pooled multi-parameter tests across imputations	pool_test