Package index • targeted

All functions
`ML()`	ML model
`NB-class`	NB class object
`NB()`	Naive Bayes
`RATE()`	Responder Average Treatment Effect
`RATE.surv()`	Responder Average Treatment Effect
`SL()`	SuperLearner wrapper for ml_model
`scoring()`	Predictive model scoring
`aipw()`	AIPW estimator
`alean()`	Assumption Lean inference for generalized linear model parameters
`ate()`	AIPW (doubly-robust) estimator for Average Treatement Effect
`calibration-class`	calibration class object
`calibration()`	Calibration (training)
`cate()`	Conditional Average Treatment Effect estimation
`cate_link()`	Conditional Relative Risk estimation
`cross_validated-class` `cross_validated`	cross_validated class object
`crr()`	Conditional Relative Risk estimation
`cv()`	Cross-validation
`design()`	Extract design matrix
`expand.list()`	Create a list from all combination of input variables
`ml_model` `predictor` `predictor_glm` `predictor_gam` `predictor_glmnet` `predictor_grf` `predictor_grf_binary` `predictor_xgboost` `predictor_xgboost_multiclass` `predictor_xgboost_count` `predictor_xgboost_cox` `predictor_xgboost_binary` `predictor_hal` `predictor_isoreg`	R6 class for prediction models
`nondom()`	Find non-dominated points of a set
`pava()`	Pooled Adjacent Violators Algorithm
`predict(<NB>)`	Predictions for Naive Bayes Classifier
`predict(<density>)`	Prediction for kernel density estimates
`predictor_sl()`	Superlearner (stacked/ensemble learner)
`riskreg()`	Risk regression
`riskreg_cens()`	Binary regression models with right censored outcomes
`softmax()`	Softmax transformation
`solve_ode()`	Solve ODE
`specials(<design>)`	Extract model component from design object
`specify_ode()`	Specify Ordinary Differential Equation (ODE)
`targeted-class` `riskreg.targeted` `ate.targeted`	targeted class object

Reference

All functions