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_intersection_sw(): Signed Wald intersection test

test_zmax_onesided(): One-sided Zmax test

truncatedscore: Scores truncated by death

weights(<superlearner>): Extract ensemble weights