rMIDAS2: Multiple Imputation with 'MIDAS2' Denoising Autoencoders
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>.
| Version: |
0.1.1 |
| 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 |
| Published: |
2026-03-12 |
| DOI: |
10.32614/CRAN.package.rMIDAS2 (may not be active yet) |
| Author: |
Thomas Robinson [aut, cre],
Ranjit Lall [aut] |
| Maintainer: |
Thomas Robinson <t.robinson7 at lse.ac.uk> |
| BugReports: |
https://github.com/MIDASverse/MIDAS2/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/MIDASverse/MIDAS2 |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.9) with the 'midasverse-midas-api'
package |
| Materials: |
NEWS |
| CRAN checks: |
rMIDAS2 results |
Documentation:
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