Package index • targeted

All functions
`ML()`	ML model
`RATE()`	Responder Average Treatment Effect
`RATE.surv()`	Responder Average Treatment Effect
`SL()`	SuperLearner wrapper for learner
`aipw()`	AIPW estimator
`alean()`	Assumption Lean inference for generalized linear model parameters
`ate()`	AIPW (doubly-robust) estimator for Average Treatment Effect
`calibration-class`	calibration class object
`calibration()`	Calibration (training)
`cate()`	Conditional Average Treatment Effect estimation
`cate_link()`	Conditional Relative Risk estimation
`constructor_shared`	Construct a learner
`cross_validated-class` `cross_validated`	cross_validated class object
`crr()`	Conditional Relative Risk estimation
`cumhaz()`	Predict the cumulative hazard/survival function for a survival model
`cv(<default>)`	Cross-validation
`cv(<learner_sl>)`	Cross-validation for learner_sl
`deprecate_arg_warn()`	Cast warning for deprecated function argument names
`deprecated_argument_names`	Deprecated argument names
`design()`	Extract design matrix
`estimate_truncatedscore()`	Estimation of mean clinical outcome truncated by event process
`expand.list()`	Create a list from all combination of input variables
`int_surv()`	Integral approximation of a time dependent function. Computes an approximation of \(\int_start^stop S(t) dt\), where \(S(t)\) is a survival function, for a selection of start and stop time points.
`learner`	R6 class for prediction models
`learner_expand_grid()`	Construct learners from a grid of parameters
`learner_gam()`	Construct a learner
`learner_glm()`	Construct a learner
`learner_glmnet_cv()`	Construct a learner
`learner_grf()`	Construct a learner
`learner_hal()`	Construct a learner
`learner_isoreg()`	Construct a learner
`learner_mars()`	Construct a learner
`learner_naivebayes()`	Construct a learner
`learner_sl()`	Construct a learner
`learner_stratify()`	Construct stratified learner
`learner_svm()`	Construct a learner
`learner_xgboost()`	Construct a learner
`ml_model`	R6 class for prediction models
`naivebayes-class`	naivebayes class object
`naivebayes()`	Naive Bayes classifier
`nondom()`	Find non-dominated points of a set
`pava()`	Pooled Adjacent Violators Algorithm
`predict(<density>)`	Prediction for kernel density estimates
`predict(<naivebayes>)`	Predictions for Naive Bayes Classifier
`predict(<superlearner>)`	Predict Method for superlearner Fits
`riskreg()`	Risk regression
`riskreg_cens()`	Binary regression models with right censored outcomes
`score(<superlearner>)`	Extract average cross-validated score of individual learners
`scoring()`	Predictive model scoring
`softmax()`	Softmax transformation
`solve_ode()`	Solve ODE
`specify_ode()`	Specify Ordinary Differential Equation (ODE)
`stratify()`	Identify Stratification Variables
`superlearner()`	Superlearner (stacked/ensemble learner)
`targeted-class` `riskreg.targeted` `ate.targeted`	targeted class object
`terms(<design>)`	Extract model component from design object
`test_intersectsignedwald()`	Signed intersection Wald test
`truncatedscore`	Scores truncated by death
`weights(<superlearner>)`	Extract ensemble weights

Reference

All functions