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_glm_density         Conditional Normal Density Estimation Given
                        Mean Predictors — with GLMs
lnr_glmer               Generalized Linear Mixed-Effects
                        ('lme4::glmer') Learner
lnr_glmnet              glmnet Learner
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_lm_density          Conditional Normal Density Estimation Given
                        Mean Predictors
lnr_lmer                Random/Mixed-Effects ('lme4::lmer') Learner
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
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
screeners               Wrapping Learners with a Screener
super_learner           Super Learner: Cross-Validation Based Ensemble
                        Learning
