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
evalITR_1.0.0.zip(r-4.7)evalITR_1.0.0.zip(r-4.6)evalITR_1.0.0.zip(r-4.5)
evalITR_1.0.0.tgz(r-4.6-any)evalITR_1.0.0.tgz(r-4.5-any)
evalITR_1.0.0.tar.gz(r-4.7-any)evalITR_1.0.0.tar.gz(r-4.6-any)
evalITR_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
evalITR/json (API)

# 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:

7.35 score 14 stars 1 packages 44 scripts 414 downloads 18 exports 116 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE210
source / vignettesOK336
linux-release-x86_64NOTE208
macos-release-arm64NOTE175
macos-oldrel-arm64NOTE208
windows-develNOTE115
windows-releaseNOTE130
windows-oldrelNOTE127
wasm-releaseOK177

Exports:AUPECAUPECcvconsist.testconsistcv.testestimate_itrevaluate_itrGATEGATEcvhet.testhetcv.testPAPDPAPDcvPAPEPAPEcvPAVPAVcvplot_estimatetest_itr

Dependencies:bartCausebitbit64bitopscaretcaToolsclassclicliprclockcodetoolscpp11crayoncvAUCdata.tabledbartsdiagramDiceKrigingdigestdistributionaldplyre1071farverforcatsforeachfuturefuture.applygamgbmgenericsggdistggplot2ggthemesglmnetglobalsgluegowergplotsgrfgtablegtoolshardhathavenhmshqreghrqglasipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixMatrixModelsModelMetricsnlmennetnnlsnumDerivparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrquadprogquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrrecipesreshape2rlangROCRrpartrqPenS7sandwichscalesshapeSparseMsparsevctrsSQUAREMstringistringrSuperLearnersurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitevroomwithrzoo

Cross-validation with multiple ML algorithms
Rendered fromcv_multiple_alg.Rmdusingknitr::rmarkdown

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

Sample Splitting with Caret/SuperLearner
Train the model with Caret | Train the model with SuperLearner
Rendered fromsample_split_caret.Rmdusingknitr::rmarkdown

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

Compare Estimated and User Defined ITR
Estimated vs. User Defined ITR | Existing Model vs. User-Defined Model
Rendered fromuser_itr_algs.Rmdusingknitr::rmarkdown

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

Cross-validation with single algorithm
Rendered fromcv_single_alg.Rmdusingknitr::rmarkdown

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

Sample Splitting
Rendered fromsample_split.Rmdusingknitr::rmarkdown

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

User Defined ITR
Rendered fromuser_itr.Rmdusingknitr::rmarkdown

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

paper_alg1
Rendered frompaper_alg1.Rmdusingknitr::rmarkdown

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

Installation
Parallelization
Rendered frominstall.Rmdusingknitr::rmarkdown

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

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