Package: evalITR 1.0.0

Michael Lingzhi Li

evalITR: Evaluating Individualized Treatment Rules

Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <arxiv:1905.05389>.

Authors:Michael Lingzhi Li [aut, cre], Kosuke Imai [aut], Jialu Li [ctb], Xiaolong Yang [ctb]

evalITR_1.0.0.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
evalITR/json (API)
NEWS

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

Bug tracker:https://github.com/michaellli/evalitr/issues

Pkgdown/docs site:https://michaellli.github.io

Datasets:
  • star - Tennessee’s Student/Teacher Achievement Ratio (STAR) project

On CRAN:

Conda:

7.29 score 14 stars 1 packages 39 scripts 257 downloads 18 exports 116 dependencies

Last updated from:50d68e9c98. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE196
source / vignettesOK324
linux-release-x86_64NOTE178
macos-release-arm64NOTE220
macos-oldrel-arm64NOTE238
windows-develNOTE214
windows-releaseNOTE139
windows-oldrelNOTE127
wasm-releaseOK144

Exports:AUPECAUPECcvconsist.testconsistcv.testestimate_itrevaluate_itrGATEGATEcvhet.testhetcv.testPAPDPAPDcvPAPEPAPEcvPAVPAVcvplot_estimatetest_itr

Dependencies:bartCausebitbit64bitopscaretcaToolsclassclicliprclockcodetoolscpp11crayoncvAUCdata.tabledbartsdiagramDiceKrigingdigestdistributionaldplyre1071farverforcatsforeachfuturefuture.applygamgbmgenericsggdistggplot2ggthemesglmnetglobalsgluegowergplotsgrfgtablegtoolshardhathavenhmshqreghrqglasipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixMatrixModelsModelMetricsnlmennetnnlsnumDerivparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrquadprogquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrrecipesreshape2rlangROCRrpartrqPenS7sandwichscalesshapeSparseMsparsevctrsSQUAREMstringistringrSuperLearnersurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitevroomwithrzoo

Compare Estimated and User Defined ITR

Rendered fromuser_itr_algs.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-08-21
Started: 2023-06-06

Cross-validation with multiple ML algorithms

Rendered fromcv_multiple_alg.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-08-26
Started: 2023-05-26

Cross-validation with single algorithm

Rendered fromcv_single_alg.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-08-21
Started: 2023-05-26

Installation

Rendered frominstall.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-05-26
Started: 2023-05-22

paper_alg1

Rendered frompaper_alg1.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-07-13
Started: 2023-07-13

Sample Splitting

Rendered fromsample_split.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-08-21
Started: 2023-05-25

Sample Splitting with Caret/SuperLearner

Rendered fromsample_split_caret.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-08-26
Started: 2023-05-25

User Defined ITR

Rendered fromuser_itr.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2023-08-21
Started: 2023-06-05

Readme and manuals

Help Manual

Help pageTopics
Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized ExperimentsAUPEC
Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized Experiments Under Cross ValidationAUPECcv
Compute Quantities of Interest (PAPE, PAPEp, PAPDp, AUPEC, GATE, GATEcv)compute_qoi
Compute Quantities of Interest (PAPE, PAPEp, PAPDp, AUPEC, GATE, GATEcv) with user defined functionscompute_qoi_user
The Consistency Test for Grouped Average Treatment Effects (GATEs) in Randomized Experimentsconsist.test
The Consistency Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experimentsconsistcv.test
Create general argumentscreate_ml_args
Create arguments for bartMachinecreate_ml_args_bart
Create arguments for bartCausecreate_ml_args_bartc
Create arguments for causal forestcreate_ml_args_causalforest
Create arguments for LASSOcreate_ml_args_lasso
Create arguments for super learnercreate_ml_args_superLearner
Create arguments for SVMcreate_ml_args_svm
Create arguments for SVM classificationcreate_ml_args_svm_cls
Create arguments for ML algorithmscreate_ml_arguments
Estimate individual treatment rules (ITR)estimate_itr
Evaluate ITRevaluate_itr
Estimate ITR for Single Outcomefit_itr
Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized ExperimentsGATE
Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized Experiments Under Cross ValidationGATEcv
The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) in Randomized Experimentshet.test
The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experimentshetcv.test
Estimation of the Population Average Prescription Difference in Randomized ExperimentsPAPD
Estimation of the Population Average Prescription Difference in Randomized Experiments Under Cross ValidationPAPDcv
Estimation of the Population Average Prescription Effect in Randomized ExperimentsPAPE
Estimation of the Population Average Prescription Effect in Randomized Experiments Under Cross ValidationPAPEcv
Estimation of the Population Average Value in Randomized ExperimentsPAV
Estimation of the Population Average Value in Randomized Experiments Under Cross ValidationPAVcv
Plot the GATE estimateplot_estimate
Plot the AUPEC curveplot.itr
Printprint.summary.itr
Printprint.summary.test_itr
Tennessee’s Student/Teacher Achievement Ratio (STAR) projectstar
Summarize estimate_itr outputsummary.itr
Summarize test_itr outputsummary.test_itr
Conduct hypothesis teststest_itr