NEWS

targeted 0.8

Development version

removing deprecated ml_model
learner_surv_cox, learner_surv_rf, cumhaz
feature(superlearner): more robust metalearner (in case quadratic prog. fails)
feature(learner_cox): mets::phreg wrapper.
disabling openMP in RcppArmadillo
bugfix: issue with cate with repeated cross-fitting fixed.

targeted 0.7.1 (2026-01-12)

Maintenance release

targeted 0.7 (2025-12-10)

This release improves the multiple testing procedures

test_zmax_onesided: one-sided Zmax / minP test
test_intersection_sw: the following parameters for the optimization can be controlled via the control list argument: dykstra_niter sets the maximum number of iterations (default 500), dykstra_tol convergence tolerance of the alternating projection algorithm (default 1e-7), pinv_tol tolerance for calculating the pseudo-inverse matrix (default value: nrow(vcov).Machine$double.epsmax(eigenvalue)).
test_intersection_sw handle edge-case where vcov is not positive-definite. Throw warning and project onto nearest PD matrix in Frobenius norm.

Breaking changes:

design refactored. Better handling of response variables (factors) xlev argument changed to levels.
learner_xgboost updated to reflect changes in xgboost 3.1.2.1. Note, the arguments names have changed accordingly and introduces thus possible breaking changes.

Features

(cate) tibble df and data.table are converted to data.frame (#135) - (905bcf1)
(design) refactoring and better handling of formulas and response variables (#136) - (044e817)
(superlearner) standard meta-learner based on quadprog::solve.QP - (4c830b7)
(test_intersection_sw) unit tests (#137) - (10a793b)
(test_intersection_sw) add user-exposed optimization arguments and improve documentation (#134) - (4c406df)
(test_intersection_sw) constrained least squares via Dykstra's algorithm, and fast signed Wald test evaluation - (2feaa87)
Feature/zmax (#129): onesided Zmax / minP test and clean-up of test_* + summary methods (#130) - (85cd87e)

Bug Fixes

(cate) Linear calibration (8dd4ca2)
(design) Print method handling Surv + factor objects (#138) - (6ce8c8a)
(makefile) Using remotes instead of devtools, to prevent workflows from breaking (#131) - (32d6b59)
(makefile) Using remotes instead of devtools, to prevent workflows from breaking - (bc4c6a6)
(slowtest) Metalearners needs to be referenced with ::: (not exported yet) (#132) - (e2324bb)
(slowtest) Metalearners not exported - (147740a)
(learner) Fix data.table and xgboost issues - (6bd9949)
(cate) Fix bug in cate: combn - (8a1dff9)
(aipw) Fixes issues with new cate - (82ac974)

Developer

(cate) Fix partial argument matching in cate_est call (#126) - (e12efe9)
(unscoped) docs(vignettes): moving to quarto documentation (#140) - (b896eba)

targeted 0.6 (2025-10-30)

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 - (ba58a2d)
(cate) linear calibration (#119) - (05a299f)
(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 (2024-02-22)

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 (2023-12-19)

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 (2022-10-25)

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 (2020-05-15)

Maintenance release.

targeted 0.1 (2020-05-08)

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).