Man pages for agnesdeng/misle
Multiple imputation through statistical learning
| adult | Adult dataset |
| batch_iter | make batch datasets, each with size batch_size |
| createNA | Create missing value for a dataset |
| data_clean | This function is used to check some common errors of a raw... |
| denoise_decoder | Midae Decoder update function |
| denoise_encoder | Midae Encoder update function |
| feature_type | Return the type of each variable in the dataset |
| he_init | Kaiming He Initialization |
| impute.new | This function is used to impute new data using training... |
| install_misle | Install the TensorFlow backend (modified code from Keras) |
| inv.minmax_data | This function back-transform data to an output as data matrix |
| Midae | R6 class for Midae imputer |
| Midae.boot | R6 class for Midae bootstrapping imputer |
| Midae.boot2 | R6 class for Midae bootstrapping imputer2 |
| midae_init | Midae initialisation |
| midae_optimizer | midae cost function |
| midae_output | Midae output evaluation |
| minmax | scale a vector using minmax |
| minmax_data | mimax data function |
| minmax_scaler | scale a dataset and return a scaled dataframe, the colmin and... |
| misle | mivae: multiple imputation using variational autoencoders |
| Mivae | Mivae imputer object R6 class for Mivae imputer |
| mivae_init | Mivae initialisation |
| mivae_output | Mivae output evaluation |
| Mixgb | Multiple imputation through xgboost R6 class imputer object |
| Mixgb.train | Multiple imputation through xgboost R6 class imputer object... |
| onehot | This function is used to convert dataframe into onehot... |
| output_structure | This function is use to identify the structure of input |
| sort_feature | This function is used to sequence the columns of the dataset,... |
| sortNA | Sort the dataset by increasing number of missing values |
| variable_class | This function is used to detect the... |
| xavier_init | Xavier Initialization using Uniform distribution |
