R: Multiple Imputation with Denoising Autoencoders

# Multiple Imputation with Denoising Autoencoders

## Documentation for package ‘rMIDAS’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## 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