Super Learning with Flexible Formulas
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Documentation for package ‘nadir’ version 0.0.1
Help Pages
| add_screener | Add a Screener to a Learner |
| binary_learners | Binary Learners in '{nadir}' |
| compare_learners | Compare Learners |
| cv_character_and_factors_schema | Cross Validation Training/Validation Splits with Characters/Factor Columns |
| cv_origami_schema | Cross-Validation with Origami |
| cv_random_schema | Assign Data to One of n_folds Randomly and Produce Training/Validation Data Lists |
| cv_super_learner | Cross-Validating a 'super_learner' |
| density_learners | Conditional Density Estimation in the '{nadir}' Package |
| determine_super_learner_weights_nnls | Determine SuperLearner Weights with Nonnegative Least Squares |
| determine_weights_for_binary_outcomes | Determine Weights Appropriately for Super Learner given Binary Outcomes |
| determine_weights_using_neg_log_loss | Determine Weights for Density Estimators for SuperLearner |
| df_to_survival_stacked | Repeat Observations for Survival Stacking |
| learners | Learners in the '{nadir}' Package |
| list_known_learners | List Known Learners |
| lnr_earth | Earth Learner |
| lnr_gam | Generalized Additive Model Learner |
| lnr_gbm | Gradient Boosting Machines Learner |
| lnr_glm | GLM Learner |
| lnr_glmer | Generalized Linear Mixed-Effects ('lme4::glmer') Learner |
| lnr_glmnet | glmnet Learner |
| lnr_glm_density | Conditional Normal Density Estimation Given Mean Predictors — with GLMs |
| lnr_hal | Highly Adaptive Lasso |
| lnr_heteroskedastic_density | Conditional Density Estimation with Heteroskedasticity |
| lnr_homoskedastic_density | Conditional Density Estimation with Homoskedasticity Assumption |
| lnr_lm | Linear Model Learner |
| lnr_lmer | Random/Mixed-Effects ('lme4::lmer') Learner |
| lnr_lm_density | Conditional Normal Density Estimation Given Mean Predictors |
| lnr_logistic | Standard Logistic Regression for Binary Classification |
| lnr_mean | Mean Learner |
| lnr_multinomial_nnet | 'nnet::multinom' Multinomial Learner |
| lnr_multinomial_vglm | 'VGAM::vglm' Multinomial Learner |
| lnr_nnet | Use nnet for Binary Classification |
| lnr_ranger | ranger Learner |
| lnr_ranger_binary | ranger Learner for Binary Outcomes |
| lnr_rf | randomForest Learner |
| lnr_rf_binary | Use Random Forest for Binary Classification |
| lnr_xgboost | XGBoost Learner |
| make_learner_names_unique | Make Unique Learner Names |
| multiclass_learners | Multiclass Learners in '{nadir}' |
| nadir_supported_types | Outcome types supported by '{nadir}' |
| negative_log_loss | Negative Log Loss |
| negative_log_loss_for_binary | Negative Log Loss for Binary |
| predict.nadir_sl_model | Predict from a 'nadir::super_learner()' model |
| screeners | Wrapping Learners with a Screener |
| screener_cor | Correlation Threshold Based Screening |
| screener_cor_top_n | Correlation Threshold Based Screening |
| screener_t_test | t-test Based Screening: Thresholds on p.values and/or t statistics |
| super_learner | Super Learner: Cross-Validation Based Ensemble Learning |