Package: personalized 0.2.8

personalized: Estimation and Validation Methods for Subgroup Identification and Personalized Medicine

Provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.

Authors:Jared Huling [aut, cre], Aaron Potvien [ctb], Alexandros Karatzoglou [cph], Alex Smola [cph]

personalized_0.2.8.tar.gz
personalized_0.2.8.zip(r-4.7)personalized_0.2.8.zip(r-4.6)personalized_0.2.8.zip(r-4.5)
personalized_0.2.8.tgz(r-4.6-any)personalized_0.2.8.tgz(r-4.5-any)
personalized_0.2.8.tar.gz(r-4.7-any)personalized_0.2.8.tar.gz(r-4.6-any)
personalized_0.2.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
personalized/json (API)
NEWS

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

Bug tracker:https://github.com/jaredhuling/personalized/issues

Pkgdown/docs site:https://jaredhuling.org

Datasets:
  • LaLonde - National Supported Work Study Data

On CRAN:

Conda:

causal-inferenceheterogeneity-of-treatment-effectindividualized-treatment-rulespersonalized-medicineprecision-medicinesubgroup-identificationtreatment-effectstreatment-scoring

7.87 score 33 stars 1 packages 126 scripts 641 downloads 11 exports 77 dependencies

Last updated from:da31260c36. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK255
source / vignettesOK245
linux-release-x86_64OK257
macos-release-arm64OK161
macos-oldrel-arm64OK158
windows-develOK313
windows-releaseOK290
windows-oldrelOK252
wasm-releaseOK124

Exports:check.overlapcreate.augmentation.functioncreate.propensity.functionfit.subgroupplotComparesubgroup.effectssummarize.subgroupstreat.effectstreatment.effectsvalidate.subgroupweighted.ksvm

Dependencies:askpassbase64encbslibcachemclicodetoolscpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomeforeachfsgenericsggplot2glmnetgluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMatrixmemoisemgcvmimenlmeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownS7sassscalesshapestringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxgboostyaml

Estimation of Flexible ITRs with xgboost

Jared Huling

Rendered fromfitting_itrs_with_xgboost.Rmdusingknitr::rmarkdownon May 08 2026.

Last update: 2022-09-02
Started: 2022-06-14

Multi-category Treatments with personalized

Jared Huling

Rendered frommulticategory_treatments_with_personalized.Rmdusingknitr::rmarkdownon May 08 2026.

Last update: 2022-06-22
Started: 2019-09-28

Usage of the Personalized Package

Jared Huling

Rendered fromusage_of_the_personalized_package.Rmdusingknitr::rmarkdownon May 08 2026.

Last update: 2022-06-27
Started: 2017-05-25

Utilities for Improving Estimation Efficiency via Augmentation and for Propensity Score Estimation

Jared Huling

Rendered fromefficiency_augmentation_personalized.Rmdusingknitr::rmarkdownon May 08 2026.

Last update: 2022-06-27
Started: 2019-11-06

Readme and manuals

Help Manual

Help pageTopics
Check propensity score overlapcheck.overlap
Creation of augmentation functionscreate.augmentation.function
Creation of propensity fitting functioncreate.propensity.function
Fitting subgroup identification modelsfit.subgroup
National Supported Work Study DataLaLonde
Plotting results for fitted subgroup identification modelsplot.subgroup_fitted plot.subgroup_validated
Plot a comparison results for fitted or validated subgroup identification modelsplotCompare
Function to predict either benefit scores or treatment recommendationspredict.subgroup_fitted predict.wksvm
Printing individualized treatment effectsprint.individual_treatment_effects
Printing results for fitted subgroup identification modelsprint.subgroup_fitted print.subgroup_summary print.subgroup_validated
Computes treatment effects within various subgroupssubgroup.effects
Summarizing covariates within estimated subgroupssummarize.subgroups summarize.subgroups.default summarize.subgroups.subgroup_fitted
Summary of results for fitted subgroup identification modelssummary.subgroup_fitted summary.wksvm
Calculation of covariate-conditional treatment effectstreat.effects treatment.effects treatment.effects.default treatment.effects.subgroup_fitted
Validating fitted subgroup identification modelsvalidate.subgroup
Fit weighted kernel svm model.weighted.ksvm