Package: targeted 0.8

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>).

Authors:Klaus K. Holst [aut, cre], Benedikt Sommer [aut], Andreas Nordland [aut], Christian B. Pipper [ctb]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
targeted/json (API)

# Install 'targeted' in R:
install.packages('targeted', repos = c('https://kkholst.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kkholst/targeted/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

causal-inferencedouble-robustestimationsemiparametric-estimationstatisticsquartoopenblascpp

8.84 score 14 stars 1 packages 34 scripts 2.7k downloads 59 exports 24 dependencies

Last updated from:bd6a663e70. Checks:11 ERROR, 2 OK. Indexed: yes.

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source / vignettesOK473
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macos-release-arm64ERROR310
macos-release-x86_64ERROR788
macos-oldrel-arm64ERROR283
macos-oldrel-x86_64ERROR646
windows-develERROR388
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wasm-releaseOK217

Exports:aipwaleanatecalibratecalibrationcateclustercumhazcvdesignestimateestimate_truncatedscoreexpand.listICisoregwlearnerlearner_expand_gridlearner_gamlearner_glmlearner_glmnet_cvlearner_grflearner_hallearner_isoreglearner_marslearner_naivebayeslearner_sllearner_stratifylearner_surv_coxlearner_surv_rflearner_svmlearner_xgboostmetalearner_convexcombmetalearner_discretemetalearner_nnlsmoinaivebayesnondomparameterpavapredict_filter_boundpredict_filter_bound_dynamicRATEriskregriskreg_censriskreg_fitriskreg_mlescorescoringsimSLsoftmaxsolve_odespecify_odestratastratifysuperlearnerSurvtest_intersection_swtest_zmax_onesided

Dependencies:abindclicodetoolsdigestfuturefuture.applyglobalslatticelavalistenvMatrixmetsmvtnormnumDerivparallellyprogressrquadprogR6RcppRcppArmadillorlangSQUAREMsurvivaltimereg

Average Treatment Effect Estimation
Introduction | Continuous outcome | Simulated data | Parametric estimation | Cross-fitting | Flexible nuisance models | Binary outcome | Estimation | Conditional average treatment effects | Session info
Rendered fromate.qmdusingquarto::html

Last update: 2026-07-15
Started: 2026-07-15

Prediction models
Introduction | Built-in learners | Usage | Update formula | Cross-validation | Prediction filter | Defining new learners | Fit function with formula and data arguments | Fit function with x and y arguments | Fit function with single data argument | Specials in formula
Rendered frompredictionclass.qmdusingquarto::html

Last update: 2026-04-08
Started: 2025-11-26

Estimating a relative risk or risk difference with a binary exposure
Introduction | Simulation | Estimation | MLE | DRE | Interactions | Risk-difference | Influence-function | SessionInfo
Rendered fromriskregression.qmdusingquarto::html

Last update: 2025-11-26
Started: 2025-11-26

Readme and manuals

Help Manual

Help pageTopics
AIPW estimatoraipw
Assumption Lean inference for generalized linear model parametersalean
AIPW (doubly-robust) estimator for Average Treatment Effectate
Calibration (training)calibrate calibration
calibration class objectcalibration-class
Conditional Average Treatment Effect estimationcate
cross_validated class objectcross_validated cross_validated-class
Predict the cumulative hazard/survival function for a survival modelcumhaz
Cross-validationcv cv.default
Cross-validation for learner_slcv.learner_sl
Cast warning for deprecated function argument namesdeprecate_arg_warn
Extract design matrixdesign
Estimation of mean clinical outcome truncated by event processestimate_truncatedscore
Create a list from all combination of input variablesexpand.list
Fit survival nuisance modelsfit_survival_models
Integral approximation of a time dependent function.int_surv
R6 class for prediction modelslearner
Construct learners from a grid of parameterslearner_expand_grid
Construct a learnerlearner_gam
Construct a learnerlearner_glm
Construct a learnerlearner_glmnet_cv
Construct a learnerlearner_grf
Construct a learnerlearner_hal
Construct a learnerlearner_isoreg
Construct a learnerlearner_mars
Construct a learnerlearner_naivebayes
Construct a learnerlearner_sl
Construct stratified learnerlearner_stratify
Construct a learnerlearner_surv_cox
Construct a learnerlearner_surv_rf
Construct a learnerlearner_svm
Construct a learnerlearner_xgboost
Convex combination meta learnermetalearner_convexcomb
Discrete meta learnermetalearner_discrete
Non-negative least squares meta learnermetalearner_nnls
Average Treatment Effect Estimation with Missing Outcome Imputationmoi
Naive Bayes classifiernaivebayes
naivebayes class objectnaivebayes-class
Find non-dominated points of a setnondom
Pooled Adjacent Violators Algorithmisoreg isoregw pava
Prediction filter bounding predictions to fixed rangepredict_filter_bound
Prediction filter bounding predictions to the observed response rangepredict_filter_bound_dynamic
Prediction for kernel density estimatespredict.density
Predictions for Naive Bayes Classifierpredict.naivebayes
Predict Method for superlearner Fitspredict.superlearner
Responder Average Treatment EffectRATE
Responder Average Treatment EffectRATE.surv
Calculate the right censoring augmentation integralrcai
Risk regressionriskreg riskreg_fit riskreg_mle
Binary regression models with right censored outcomesriskreg_cens
Extract average cross-validated score of individual learnersscore.superlearner
Predictive model scoringscoring
SuperLearner wrapper for learner (defunct)SL
Softmax transformationsoftmax
Solve ODEsolve_ode
Specify Ordinary Differential Equation (ODE)specify_ode
Identify Stratification Variablesstratify
Superlearner (stacked/ensemble learner)superlearner
Treatment level estimating functions for survival outcomes under right censoringsurvival_treatment_level_estfun
targeted class objectate.targeted riskreg.targeted targeted-class
Extract model component from design objectterms.design
Signed Wald intersection testtest_intersection_sw
One-sided Zmax testtest_zmax_onesided
Scores truncated by deathtruncatedscore
Extract ensemble weightsweights.superlearner