CRAN/E | evalITR

evalITR

Evaluating Individualized Treatment Rules

Installation

About

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) .

github.com/MichaelLLi/evalITR
michaellli.github.io/evalITR/
jialul.github.io/causal-ml/
Bug report File report

Key Metrics

Version 1.0.0
R ≥ 3.5.0
Published 2023-08-25 100 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks evalITR results
Language en-US

Downloads

Last 24 hours 3 -77%
Last 7 days 79 +30%
Last 30 days 294 +16%
Last 90 days 831 +20%
Last 365 days 3.031 -28%

Maintainer

Maintainer

Michael Lingzhi Li

mili@hbs.edu

Authors

Michael Lingzhi Li

aut / cre

Kosuke Imai

aut

Jialu Li

ctb

Xiaolong Yang

ctb

Material

README
NEWS
Reference manual
Package source

In Views

CausalInference

Vignettes

Cross-validation with multiple ML algorithms
Cross-validation with single algorithm
Installation
paper_alg1
Sample Splitting
Sample Splitting with Caret/SuperLearner
User Defined ITR
Compare Estimated and User Defined ITR

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

evalITR archive

Depends

dplyr ≥ 1.0
MASS ≥ 7.0
Matrix ≥ 1.0
quadprog ≥1.0
R ≥ 3.5.0
stats

Imports

caret
cli
e1071
forcats
gbm
ggdist
ggplot2
ggthemes
glmnet
grf
haven
purrr
rlang
rpart
rqPen
scales
utils
bartCause
SuperLearner

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