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.

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.

Awesome papers on Missing Data

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

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)

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

Multiple Imputation in Causal Graph Discovery

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

XeroGraph is a Python package developed for researchers and data scientists to analyze, visualize and impute missing data in datasets.