Multivariate Imputation by Chained Equations

Flexible Imputation of Missing Data - bookdown source

R enviroment - fast imputations 🐉

Some Additional Multiple Imputation Functions, Especially for 'mice'.

Una herramienta para el uso y análisis de los datos de Conflicto armado en Colombia resultantes del proyecto conjunto JEP-CEV-HRDAG.

Awesome papers on Missing Data

a package for missing data handling via multiple imputation by chained equations in Julia. It is heavily based on the R package {mice} by Stef van Buuren, Karin Groothuis-Oudshoorn and collaborators.

psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets

Multiple Imputation with GAMLSS

Code of the experiments ran in our GigaScience article: "Benchmarking missing-values approaches for predictive models on health databases".

Use tidyverse functions to correctly meld and pool multiply imputed model output

Multiple Imputation in Causal Graph Discovery

A package for synthetic data generation for imputation using single and multiple imputation methods.

Source Code for Paper "Bayesian MI-LASSO for variable selection on multiply-imputed data" (Arxiv: https://arxiv.org/abs/2211.00114)

Model Based Treatment of Missing Data

Code and supplementary materials for the manuscript "Multiple imputation for cause-specific Cox models: assessing methods for estimation and prediction" (2022, Statistical Methods in Medical Research)

Repository for the development of the rbmiUtils package which extends the {rbmi} package for use within clinical trials.

Code and supplementary materials for the manuscript "Handling missing covariate data in clinical studies in haematology" (2023, Best Practice & Research Clinical Haematology)

Extend broom's tidy() and glance() to work with lists of multiply imputed regression models