Multiple Imputation with 'MIDAS2' Denoising Autoencoders R package rMIDAS2 version 0.1.1 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) <doi:10.1017/pan.2020.49> and Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>. Software Depends: R (>= 4.1.0) Imports: curl, httr2 (>= 1.0.0), processx (>= 3.8.0), rlang (>= 1.1.0) Suggests: arrow, jsonlite, reticulate, testthat (>= 3.0.0), knitr, rmarkdown Thomas Robinson <t.robinson7@lse.ac.uk> Comprehensive R Archive Network (CRAN) Thomas Robinson [aut, cre], Ranjit Lall [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=rMIDAS2/LICENSE) 2026-03-12 application/tgz https://CRAN.R-project.org/package=rMIDAS2 doi:10.32614/CRAN.package.rMIDAS2