Targeted Inference
AIPW estimator
Assumption Lean inference for generalized linear model parameters
AIPW (doubly-robust) estimator for Average Treatment Effect
calibration class object
Calibration (training)
Conditional Relative Risk estimation
Conditional Average Treatment Effect estimation
Construct a learner
cross_validated class object
Conditional Relative Risk estimation
Predict the cumulative hazard/survival function for a survival model
Cross-validation
Cross-validation for learner_sl
Cast warning for deprecated function argument names
Deprecated argument names
Extract design matrix
Estimation of mean clinical outcome truncated by event process
Create a list from all combination of input variables
Integral approximation of a time dependent function. Computes an appro...
Construct learners from a grid of parameters
Construct a learner
Construct a learner
Construct a learner
Construct a learner
Construct a learner
Construct a learner
Construct a learner
Construct a learner
Construct a learner
Construct stratified learner
Construct a learner
Construct a learner
R6 class for prediction models
R6 class for prediction models
ML model
naivebayes class object
Naive Bayes classifier
Find non-dominated points of a set
Pooled Adjacent Violators Algorithm
Prediction for kernel density estimates
Predictions for Naive Bayes Classifier
Predict Method for superlearner Fits
Responder Average Treatment Effect
Responder Average Treatment Effect
Objects exported from other packages
Binary regression models with right censored outcomes
Risk regression
Extract average cross-validated score of individual learners
Predictive model scoring
SuperLearner wrapper for learner
Softmax transformation
Solve ODE
Specify Ordinary Differential Equation (ODE)
Identify Stratification Variables
Superlearner (stacked/ensemble learner)
targeted class object
targeted: Targeted Inference
Extract model component from design object
Signed Wald intersection test
Extract ensemble weights
Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).