rMIDAS - Multiple Imputation with Denoising Autoencoders
A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>. This package is deprecated in favor of 'rMIDAS2'; it remains available for existing workflows but will receive only compatibility and documentation updates.
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deep-learningimputation-methodsneural-networkreticulatetensorflow
6.75 score 37 stars 38 scripts 562 downloadscitestR - Conditional Independence of Missingness Test
Tests whether missingness in explanatory variables is conditionally independent of the outcome, given observed data. Uses multiply-imputed datasets and cross-validated classifiers to produce a test statistic and p-value, with a sensitivity parameter (kappa) for calibrating interpretation. Wraps the 'citest' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime.
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5.04 score 1 stars 3 scripts 453 downloads