Multiple Imputation with Denoising Autoencoders

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Documentation for package ‘rMIDAS’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

add_bin_labels	Reverse numeric conversion of binary vector
add_missingness	Apply MAR missingness to data
coalesce_one_hot	Coalesce one-hot encoding back to a single variable
col_minmax	Scale numeric vector between 0 and 1
combine	Estimate and combine regression models from multiply-imputed data
complete	Impute missing values using imputation model
convert	Pre-process data for Midas imputation
delete_rMIDAS_env	Delete the rMIDAS Environment and Configuration
import_midas	Instantiate Midas class
midas_setup	Manually set up Python connection
mid_py_setup	Configure python for MIDAS imputation
na_to_nan	Replace NA missing values with NaN
overimpute	Perform overimputation diagnostic test
python_configured	Check whether Python is capable of executing example code
python_init	Initialise connection to Python
reset_rMIDAS_env	Reset the rMIDAS Environment Configuration
set_python_env	Manually select python binary
skip_if_no_numpy	Skip test where 'numpy' not available.
train	Train an imputation model using Midas
undo_minmax	Reverse minmax scaling of numeric vector