col_minmax

Helper function to scale numeric variables. Aids convergence of Midas model.

preprocessing

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

col_minmax function

<dl><dt>x</dt>
<dd>A numeric vector or column.</dd></dl>

Arguments

Scale numeric vector between 0 and 1 — col_minmax

<dl>

<dt>x</dt>
<dd>A numeric vector or column.</dd>

</dl>

col_minmax: Scale numeric vector between 0 and 1

Description

Usage

Value

Arguments

Examples