na_to_nan

Helper function to convert <code>NA</code> values in a data.frame to <code>NaN</code>. This ensures the correct conversion of missing values when reticulate converts R objects to their Python equivalent. See the reticulate package documentation on type conversions for more information.

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

na_to_nan function

<dl><dt>df</dt>
<dd>Data frame, or object coercible to one.</dd></dl>

Arguments

Replace NA missing values with NaN — na_to_nan

<dl>

<dt>df</dt>
<dd>Data frame, or object coercible to one.</dd>

</dl>

na_to_nan: Replace NA missing values with NaN

Description

Usage

Value

Arguments

Examples