• Development version
  • repeated cross-fitting in cate function via the new ‘rep’ argument
  • First argument to ml_model can be a character defining the response-variable (optional)
  • predictor wrapper, and predictor_sl, predictor_glm, …
  • cate now also returns the expected potential outcomes and influence functions
  • Bug-fix in the ml_model$update() method
  • The default scoring method for cv now only switches to log-score+brier score when the response is a factor. Custom model-scoring function (cv argument modelscore) automatically gets ‘weights’ appended to the formal-arguments.
  • alean: Assumption Lean inference for generalized linear model parameters
  • ate now supports general family argument
  • cate now supports parallelization via the future or parallel package
  • ml_model refactored. ML new wrapper for various machine learning models.
  • cv parallelization (future or parallel package)
  • riskreg_cens cumulative risk, restricted mean survival predictions (censored unbiased regression estimates)
  • Conditional average treatment estimator cate, crr
  • Generic prediction model class ml_model
  • design
  • SuperLearner wrapper SL
  • Average Treatment among responders RATE
  • Weighted Naive Bayes classifer with NB
  • Pooled adjacent violator algorithm pava
  • ODE solver ode_solve
  • Calibration calibration
  • Cross-validation cv
  • ace method updated and renamed to ate
  • Maintenance release.
  • Initialization of the new package targeted with implementation of augmented inverse probability weighting methods for estimation with missing data and causal inference (aipw, ace), and double robust methods for risk regression with binary exposure variables (riskreg).