Package index • nadir

All functions

add_screener(): Add a Screener to a Learner

binary_learners: Binary Learners in nadir

check_simple_lhs(): Validate that a formula has a simple left‐hand side check_simple_lhs( ~ x1 + x2) # errors because no lhs

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_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_rf(): randomForest Learner

lnr_rf_binary(): Use Random Forest for Binary Classification

lnr_xgboost(): XGBoost Learner

nadir_supported_types: Outcome types supported by nadir

negative_log_loss(): Negative Log Loss

negative_log_loss_for_binary(): Negative Log Loss for Binary

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