Man pages for targeted
Targeted Inference
| aipw | AIPW estimator |
| alean | Assumption Lean inference for generalized linear model... |
| ate | AIPW (doubly-robust) estimator for Average Treatment Effect |
| calibration | Calibration (training) |
| calibration-class | calibration class object |
| cate | Conditional Average Treatment Effect estimation |
| cross_validated-class | cross_validated class object |
| cumhaz | Predict the cumulative hazard/survival function for a... |
| cv.default | Cross-validation |
| cv.learner_sl | Cross-validation for learner_sl |
| deprecate_arg_warn | Cast warning for deprecated function argument names |
| design | Extract design matrix |
| estimate_truncatedscore | Estimation of mean clinical outcome truncated by event... |
| expand.list | Create a list from all combination of input variables |
| fit_survival_models | Fit survival nuisance models |
| int_surv | Integral approximation of a time dependent function. |
| 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_surv_cox | Construct a learner |
| learner_surv_rf | Construct a learner |
| learner_svm | Construct a learner |
| learner_xgboost | Construct a learner |
| metalearner_convexcomb | Convex combination meta learner |
| metalearner_discrete | Discrete meta learner |
| metalearner_nnls | Non-negative least squares meta learner |
| moi | Average Treatment Effect Estimation with Missing Outcome... |
| moi_missing | Mean Imputation Among Missing Outcomes |
| naivebayes | Naive Bayes classifier |
| naivebayes-class | naivebayes class object |
| nondom | Find non-dominated points of a set |
| pava | Pooled Adjacent Violators Algorithm |
| predict.density | Prediction for kernel density estimates |
| predict_filter_bound | Prediction filter bounding predictions to fixed range |
| predict_filter_bound_dynamic | Prediction filter bounding predictions to the observed... |
| predict.naivebayes | Predictions for Naive Bayes Classifier |
| predict.superlearner | Predict Method for superlearner Fits |
| RATE | Responder Average Treatment Effect |
| RATE.surv | Responder Average Treatment Effect |
| rcai | Calculate the right censoring augmentation integral |
| reexports | Objects exported from other packages |
| 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 |
| SL | SuperLearner wrapper for learner (defunct) |
| softmax | Softmax transformation |
| solve_ode | Solve ODE |
| specify_ode | Specify Ordinary Differential Equation (ODE) |
| stratify | Identify Stratification Variables |
| superlearner | Superlearner (stacked/ensemble learner) |
| survival_treatment_level_estfun | Treatment level estimating functions for survival outcomes... |
| targeted-class | targeted class object |
| targeted-package | targeted: Targeted Inference |
| 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 |
