Package index • nadir

All functions

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

extract_y_variable(): Extract Y Variable from a list of Regression Formulas and Learners

learners: Learners in the nadir Package

lnr_glm_density(): Conditional Normal Density Estimation Given Mean Predictors — with GLMs

lnr_glmnet(): glmnet Learner

lnr_heteroskedastic_density(): Conditional Density Estimation with Heteroskedasticity

lnr_homoskedastic_density(): Conditional Density Estimation with Homoskedasticity Assumption

lnr_lm_density(): Conditional Normal Density Estimation Given Mean Predictors

negative_log_loss(): Negative Log Loss

parse_extra_learner_arguments(): Parse Extra Arguments

parse_formulas(): Parse Formulas for Super Learner

softmax(): Softmax

super_learner(): Super Learner: Cross-Validation Based Ensemble Learning