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
mid_py_setup            Configure python for MIDAS imputation
midas_setup             Manually set up Python connection
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
rMIDAS-package          rMIDAS: Multiple Imputation with Denoising
                        Autoencoders
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
