targeted 0.6

This release introduces a new learner class replacing the previous ML constructor.

• constructors for commonly used regression and classification models are also implemented: learner_glm, learner_gam, learner_grf, learner_hal, learner_glmnet_cv, learner_svm, learner_xgboost, learner_mars, learner_isoreg, learner_naivebayes
• new ensemble models (super-learners) available with superlearner and learner_sl
• learner_stratify: implementation of learner that can stratifies base-learner on categorical predictor
• learner_expand_grid: utility function to construct learners

Improved implementation of cate with repeated cross-fitting via the new ‘rep’ argument. Linear calibration via the calibration.model argument doi:10.1093/biomet/asaf029.

Implementation of estimators for joint modelling of time-to-event (CIF) and clinical outcome truncated by competing risk (arXiv.2502.03942): estimate_truncatedscore.

test_intersection_sw Constrained least squares via Dykstra’s algorithm, and fast signed wald test evaluation.

Features

• (test_sw) signed wald intersection test
• (cate) linear calibration
• (superlearner): standard meta-learner based on quadprog::solve.QP
• (cv) cross-validation cv method for superlearner objects (#64) - (1d58b26)
• (design) Fixing how specials is handled and passed to learner functions - (ab46749)
• (design) Adding print.design (#94) - (20eb170)
• (learner) learner_stratify implementation of learner that can stratifies base-learner on categorical predictor (d561ea1)
• (learner) [breaking] changing formula public field to active binding (#98) - (1505453)
• (learner) [breaking] removing response.arg and x.arg arguments from learner$new() (#92) - (4043dd7)
• (learner) adding new summary method to provide more details than print method (#87) - (d12a581)
• (learner) changed behaviour of learner$design to return not only ‘x’ matrix but everything including ‘specials’ (#76) - (ca74abb)
• (superlearner) new ensemble models (super-learners) (#104)
• (learner) learner_expand_grid utility function to construct learners (#96) - (3ae461a)
• Generalized Additive Models learner_gam (#77) - (de2ec2b)
• Highly Adaptive Lasso learner_hal (#75) - (62c4941)
• Elastic net learner_glmnet_cv (#74) - (67ba241)
• Generalized Linear Models learner_glm (#63) - (0d2663a)
• Naive Bayes classifier learner_naivebayes (#88) - (2cbe979)
• Generalized Random Forest learner_grf (#84) - (82f76c8)
• Support Vector Regression learner_svm (#83) - (4b28b30)
• Isotonic regression learner_isoreg (#82) - (e409b58)
• XGBoost learner_xgboost (#80) - (72ee414)
• Multivariate Adaptive Regression Splines learner_mars (#79) - (0019060)
• Super-Learner learner_sl (#78) - (03a81d2)
• Adding new learner R6 class to replace ml_model (#68) - (86c44fd)
• Improved riskreg_cens estimator (#62) - (7aef75f)
• truncatedscore default is now to estimate P(T>=t) instead of CIF (#46) (b315645)
• (cv) silent arg (#34) - (bb3d782)
• Feature/truncatedscore Implementation of estimators for joint modelling of time-to-event (CIF) and clinical outcome truncated by competing risk. (#13)

Documentation

• (learner) Harmonize documentation of all learner constructors (#93) - (1d37075)
• (learner) New vignette on prediction model class (#4) - (08bedd4)
• (RATE) Fix linter and re-use roxygen documentation (#45) - (a31a37c)

Bug Fixes

• (cate) Now sets correct mc.cores argument in mclapply (#24) - (260ded8)

Developer

• Adding add_dots utility function (#2) - (bb21da4)
• Adding .cliff.toml (#47) - (47d7038)
• Github workflows (#33) - (bcd50bd)
• Adding custom function to inform users about deprecated function arguments (#32) - (d0865a2)
• Makefile + repository cleaning (#23) - (fa39827)
• Switch from testthat to tinytest for unit testing of R package (#6) - (be86072)
• Adding .lintr config for R code linter - (7fe7b56)

targeted 0.5

• 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.

targeted 0.4

• 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)

targeted 0.3

• Conditional average treatment estimator cate, crr
• Generic prediction model class ml_model
• design
• SuperLearner wrapper SL
• Average Treatment among responders RATE

targeted 0.2

• 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

targeted 0.1.1

• Maintenance release.

targeted 0.1

• 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).