From missing mechanism of data to data imputation

Multiple imputation with chained equation implemented from scratch. This is a low performance implementation meant for pedagogical purposes only.

Este estudio investiga la efectividad de la imputación múltiple en el análisis factorial confirmatorio (AFC) con datos de liderazgo, donde se simularon valores perdidos (MCAR) en un 40% de la muestra.

Inject Missing Values Not-At-Random to Simulated Likert Data Sets

R package for controlled multiple imputation of ordinal or binary responses with missing data in clinical study

Replication code for "Connecting Leaves to the Forest" academic project.

Code for Master's Thesis Data Science & Society

Landing page for amices.org

Machine Learning in Official Statistics

Code repository for the manuscript 'Multiple imputation of missing covariates when using the Fine–Gray model' (under review)

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))

Analyses of Fear Generalization Task in SAM study

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.

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)

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)