Targeted Inference
AIPW estimator
Assumption Lean inference for generalized linear model parameters
AIPW (doubly-robust) estimator for Average Treatement Effect
calibration class object
Calibration (training)
Conditional Average Treatment Effect estimation
Conditional Relative Risk estimation
cross_validated class object
Conditional Relative Risk estimation
Cross-validation
Extract design matrix
Create a list from all combination of input variables
ML model
R6 class for prediction models
NB class object
Naive Bayes
Find non-dominated points of a set
Pooled Adjacent Violators Algorithm
Prediction for kernel density estimates
Predictions for Naive Bayes Classifier
Responder Average Treatment Effect
Responder Average Treatment Effect
Risk regression
Binary regression models with right censored outcomes
Predictive model scoring
SuperLearner wrapper for ml_model
Softmax transformation
Solve ODE
Specify Ordinary Differential Equation (ODE)
targeted class object
targeted: Targeted Inference
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>).