Man pages for rMIDAS
Multiple Imputation with Denoising Autoencoders
| 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... |
| 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 |
| rMIDAS-package | rMIDAS: Multiple Imputation with Denoising Autoencoders |
| 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 |
