missing-not-at-random

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nbip / notMIWAE

Code accompanying the notMIWAE paper

missing-data variational-inference importance-sampling missing-values iwae missing-data-imputation importance-weighted-autoencoder deep-generative-modelling missing-not-at-random

Updated Jan 28, 2021
Jupyter Notebook

xsswang / remiod

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

statistics bayesian missing-data glm r-package mcmc jags multiple-imputation generalized-linear-models ordinal-regression reference-based missing-not-at-random missing-at-random pattern-mixture-model non-ignorable control-based jump-to-reference copy-reference delta-adjustment

Updated Feb 18, 2023
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madelinecraft / Mitigating-the-Influence-of-Missing-Data

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This project demonstrates that inaccurate conclusions may be drawn from partially observed data and proposes a strategy for mitigating such conclusions.

missing-data latent-variable-models structural-equation-models missing-not-at-random pattern-mixture-model

Updated Apr 16, 2022
R

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