# Targeted Inference

## Documentation for package ‘targeted’ version 0.6

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Code demos. Use demo() to run them.
- Package NEWS.

## Help Pages

aipw | AIPW estimator
alean | Assumption Lean inference for generalized linear model parameters
ate | AIPW (doubly-robust) estimator for Average Treatment Effect
ate.targeted | targeted class object
calibrate | Calibration (training)
calibration | Calibration (training)
calibration-class | calibration class object
cate | Conditional Average Treatment Effect estimation
cate_link | Conditional Relative Risk estimation
constructor_shared | Construct a learner
cross_validated | cross_validated class object
cross_validated-class | cross_validated class object
crr | Conditional Relative Risk estimation
cumhaz | Predict the cumulative hazard/survival function for a survival model
cv | Cross-validation
cv.default | Cross-validation
cv.learner_sl | Cross-validation for learner_sl
deprecated_argument_names | Deprecated argument names
deprecate_arg_warn | Cast warning for deprecated function 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.
isoreg | Pooled Adjacent Violators Algorithm
isoregw | Pooled Adjacent Violators Algorithm
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 | ML model
ml_model | R6 class for prediction models
naivebayes | Naive Bayes classifier
naivebayes-class | naivebayes class object
NB | 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
RATE | Responder Average Treatment Effect
RATE.surv | Responder Average Treatment Effect
riskreg | Risk regression
riskreg.targeted | targeted class object
riskreg_cens | Binary regression models with right censored outcomes
riskreg_fit | Risk regression
riskreg_mle | Risk regression
score.superlearner | Extract average cross-validated score of individual learners
scoring | Predictive model scoring
SL | SuperLearner wrapper for learner
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 | targeted class object
terms.design | Extract model component from design object
test_intersection_sw | Signed Wald intersection test
truncatedscore | Scores truncated by death
weights.superlearner | Extract ensemble weights