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]

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evalITR.pdf |evalITR.html✨
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

Datasets:

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

On CRAN:

18 exports 13 stars 1.82 score 118 dependencies 36 scripts 248 downloads

Last updated 1 years agofrom:50d68e9c98. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 10 2024
R-4.5-win	NOTE	Sep 10 2024
R-4.5-linux	NOTE	Sep 10 2024
R-4.4-win	NOTE	Sep 10 2024
R-4.4-mac	NOTE	Sep 10 2024
R-4.3-win	NOTE	Sep 10 2024
R-4.3-mac	NOTE	Sep 10 2024

Exports:AUPEC AUPECcv consist.test consistcv.test estimate_itr evaluate_itr GATE GATEcv het.test hetcv.test PAPD PAPDcv PAPE PAPEcv PAV PAVcv plot_estimate test_itr

Dependencies:bartCause bit bit64 bitops caret caTools class cli clipr clock codetools colorspace cpp11 crayon cvAUC data.table dbarts diagram DiceKriging digest distributional dplyr e1071 fansi farver forcats foreach future future.apply gam gbm generics ggdist ggplot2 ggthemes glmnet globals glue gower gplots grf gtable gtools hardhat haven hms hqreg hrqglas ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lmtest lubridate magrittr MASS Matrix MatrixModels mgcv ModelMetrics munsell nlme nnet nnls numDeriv parallelly pillar pkgconfig plyr prettyunits pROC prodlim progress progressr proxy purrr quadprog quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr recipes reshape2 rlang ROCR rpart rqPen sandwich scales shape SparseM SQUAREM stringi stringr SuperLearner survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite vroom withr zoo

Compare Estimated and User Defined ITR

Rendered fromuser_itr_algs.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

Cross-validation with multiple ML algorithms

Rendered fromcv_multiple_alg.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

Cross-validation with single algorithm

Rendered fromcv_single_alg.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

Installation

Rendered frominstall.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

paper_alg1

Rendered frompaper_alg1.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

Sample Splitting

Rendered fromsample_split.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

Sample Splitting with Caret/SuperLearner

Rendered fromsample_split_caret.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

User Defined ITR

Rendered fromuser_itr.Rmdusingknitr::rmarkdownon Sep 10 2024.

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized Experiments	AUPEC
Estimation of the Area Under Prescription Evaluation Curve (AUPEC) in Randomized Experiments Under Cross Validation	AUPECcv
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 functions	compute_qoi_user
The Consistency Test for Grouped Average Treatment Effects (GATEs) in Randomized Experiments	consist.test
The Consistency Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experiments	consistcv.test
Create general arguments	create_ml_args
Create arguments for bartMachine	create_ml_args_bart
Create arguments for bartCause	create_ml_args_bartc
Create arguments for causal forest	create_ml_args_causalforest
Create arguments for LASSO	create_ml_args_lasso
Create arguments for super learner	create_ml_args_superLearner
Create arguments for SVM	create_ml_args_svm
Create arguments for SVM classification	create_ml_args_svm_cls
Create arguments for ML algorithms	create_ml_arguments
Estimate individual treatment rules (ITR)	estimate_itr
Evaluate ITR	evaluate_itr
Estimate ITR for Single Outcome	fit_itr
Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized Experiments	GATE
Estimation of the Grouped Average Treatment Effects (GATEs) in Randomized Experiments Under Cross Validation	GATEcv
The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) in Randomized Experiments	het.test
The Heterogeneity Test for Grouped Average Treatment Effects (GATEs) under Cross Validation in Randomized Experiments	hetcv.test
Estimation of the Population Average Prescription Difference in Randomized Experiments	PAPD
Estimation of the Population Average Prescription Difference in Randomized Experiments Under Cross Validation	PAPDcv
Estimation of the Population Average Prescription Effect in Randomized Experiments	PAPE
Estimation of the Population Average Prescription Effect in Randomized Experiments Under Cross Validation	PAPEcv
Estimation of the Population Average Value in Randomized Experiments	PAV
Estimation of the Population Average Value in Randomized Experiments Under Cross Validation	PAVcv
Plot the GATE estimate	plot_estimate
Plot the AUPEC curve	plot.itr
Print	print.summary.itr
Print	print.summary.test_itr
Tennessee’s Student/Teacher Achievement Ratio (STAR) project	star
Summarize estimate_itr output	summary.itr
Summarize test_itr output	summary.test_itr
Conduct hypothesis tests	test_itr