mid_py_setup

<p>This helper function checks if the required Python dependencies are installed, and if not, checks with user before installing them.
Users should not call this function directly. Users can set a custom python install using <code>set_python_env()</code> so long as this is done prior to the first call to <code>train()</code> or <code>complete()</code>.</p>

setup

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>.

Thomas Robinson

rMIDAS

Multiple Imputation with Denoising Autoencoders

Ranjit Lall

Alex Stenlake

Elviss Dvinskis

mid_py_setup function

Configure python for MIDAS imputation — mid_py_setup

Configure python for MIDAS imputation

mid_py_setup: Configure python for MIDAS imputation

Description

Usage

Arguments