R: Super Learning with Flexible Formulas

# Super Learning with Flexible Formulas

## Documentation for package ‘nadir’ version 0.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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