rMIDAS2: Multiple Imputation with 'MIDAS2' Denoising Autoencoders

2026 Dataset Open access

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Authors: Thomas Robinson, Ranjit Lall
DOI: 10.32614/cran.package.rmidas2
Cited by: 0
References: 2

Abstract

Fits 'MIDAS' denoising autoencoder models for multiple imputation of missing data, generates multiply-imputed datasets, computes imputation means, and runs Rubin's rules regression analysis. Wraps the 'MIDAS2' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. Methods are described in Lall and Robinson (2022) and Lall and Robinson (2023) .

Fields of Study & Subjects

Statistical Methods and Bayesian Inference Data Analysis with R Markov Chains and Monte Carlo Methods

Publication details

Publication date
12 March 2026
Language
EN
Document type
Dataset