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| NBLDA-package | Classifying count data using Poisson/Negative Binomial linear discriminant analysis |
| cervical | Cervical cancer data |
| control | Accessors for the 'control' slot. |
| control-method | Accessors for the 'control' slot. |
| FindBestTransform | Find the Power Transformation Parameter. |
| generateCountData | Generate Count Data |
| getShrinkedDispersions | Estimate Shrinked Overdispersions |
| inputs | Accessors for the 'input' slot. |
| inputs-method | Accessors for the 'input' slot. |
| nblda-class | 'nblda' object |
| nbldaControl | Control parameters for trained NBLDA model. |
| nbldaTrained | Accessors for the 'crossValidated' slot. |
| nbldaTrained-method | Accessors for the 'crossValidated' slot. |
| nblda_input-class | 'nblda_input' object |
| nblda_trained-class | 'nblda_trained' object |
| normalization | Accessors for the 'type' slot. |
| normalization-method | Accessors for the 'type' slot. |
| NullModel | Calculate the Normalized Counts and Related Training Parameters. |
| NullModelTest | Calculate the Normalized Counts and Related Training Parameters. |
| plot | Plot Method for the 'nblda' and 'nblda_trained' Classes |
| plot-method | Plot Method for the 'nblda' and 'nblda_trained' Classes |
| plot.nblda | Plot Method for the 'nblda' and 'nblda_trained' Classes |
| plot.nblda_trained | Plot Method for the 'nblda' and 'nblda_trained' Classes |
| predict | Extract predictions from NBLDA model |
| predict-method | Extract predictions from NBLDA model |
| predict.nblda | Extract predictions from NBLDA model |
| selectedFeatures | Accessors for the 'selectedFeatures' slot. |
| selectedFeatures-method | Accessors for the 'selectedFeatures' slot. |
| show | Show Method for the S4 classes in NBLDA Package |
| show-method | Show Method for the S4 classes in NBLDA Package |
| show.nblda | Show Method for the S4 classes in NBLDA Package |
| show.nblda_input | Show Method for the S4 classes in NBLDA Package |
| show.nblda_trained | Show Method for the S4 classes in NBLDA Package |
| trainNBLDA | Train Model over Different Tuning Parameters |