Multivariate Imputation by Chained Equations
-
Updated
Oct 3, 2024 - R
Multivariate Imputation by Chained Equations
Flexible Imputation of Missing Data - bookdown source
R enviroment - fast imputations 🐉
Some Additional Multiple Imputation Functions, Especially for 'mice'.
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
Una herramienta para el uso y análisis de los datos de Conflicto armado en Colombia resultantes del proyecto conjunto JEP-CEV-HRDAG.
psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets
Use tidyverse functions to correctly meld and pool multiply imputed model output
Code of the experiments ran in our GigaScience article: "Benchmarking missing-values approaches for predictive models on health databases".
Source Code for Paper "Bayesian MI-LASSO for variable selection on multiply-imputed data" (Arxiv: https://arxiv.org/abs/2211.00114)
A package for synthetic data generation for imputation using single and multiple imputation methods.
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)
Machine Learning in Official Statistics
Add a description, image, and links to the multiple-imputation topic page so that developers can more easily learn about it.
To associate your repository with the multiple-imputation topic, visit your repo's landing page and select "manage topics."