There are 0 repository under multiple-imputation topic.
Flexible Imputation of Missing Data - bookdown source
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.
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
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".
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)
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
Code and supplementary materials for the manuscript "Handling missing covariate data in clinical studies in haematology" (2023, Best Practice & Research Clinical Haematology)
Code and supplementary materials for the manuscript "Development and validation of a prediction model for failure of the transfemoral approach of endovascular treatment for large vessel occlusion acute ischemic stroke" (2023, Cerebrovascular Diseases))
Machine Learning in Official Statistics
XeroGraph is a Python package developed for researchers and data scientists to analyze, visualize and impute missing data in datasets.
Code repository for the manuscript 'Multiple imputation of missing covariates when using the Fine–Gray model' (submitted)
Replication code for "Connecting Leaves to the Forest" academic project.
Inject Missing Values Not-At-Random to Simulated Likert Data Sets
Code for Master's Thesis Data Science & Society
R package for controlled multiple imputation of ordinal or binary responses with missing data in clinical study
Multiple imputation with chained equation implemented from scratch. This is a low performance implementation meant for pedagogical purposes only.
From missing mechanism of data to data imputation