Package: triageR 0.2.0

triageR: Automated Machine Learning and AI Agent Tools for Clinical Prediction Modelling

Provides a streamlined workflow for building, validating, and reporting clinical prediction models. Combines standard machine learning tools with an optional AI agent that recommends appropriate statistical methods, runs sensitivity analyses, and flags common pitfalls. Includes automated generation of reports aligned with TRIPOD+AI reporting guidance (Collins et al. (2024 <doi:10.1136/bmj-2023-078378>)) for reproducible, guideline-aligned research.

Authors:Uwakmfon Paul [aut, cre, cph]

triageR_0.2.0.tar.gz
triageR_0.2.0.zip(r-4.7-any)triageR_0.2.0.zip(r-4.6-any)triageR_0.2.0.zip(r-4.5-any)
triageR_0.2.0.tgz(r-4.6-any)triageR_0.2.0.tgz(r-4.5-any)
triageR_0.2.0.tar.gz(r-4.7-any)triageR_0.2.0.tar.gz(r-4.6-any)
triageR_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
triageR/json (API)

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

Bug tracker:https://github.com/devwebwacky/triager/issues

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

On CRAN:

Conda:

quarto

4.95 score 379 downloads 13 exports 145 dependencies

Last updated from:1f51cb1ff1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK186
source / vignettesOK271
linux-releaseOK199
macos-releaseOK270
macos-oldrelOK275
windows-develOK168
windows-releaseOK131
windows-oldrelOK133
wasm-releaseOK143

Exports:tr_agent_reviewtr_check_missingtr_explaintr_fittr_fit_survivaltr_imputetr_launch_apptr_load_clinicaltr_recommend_methodtr_sensitivitytr_tripod_reporttr_validatetr_validate_survival

Dependencies:askpassbackportsbase64encbitbit64bootbroombslibcachemclassclicliprclockcodetoolscorocpp11crayoncurlDALEXdata.tablediagramdigestdoFuturedplyrellmerevaluatefarverfastmapfontawesomeforcatsforeachfsfuturefuture.applygenericsggplot2glmnetglobalsgluegowergridExtragtablehardhathavenhighrhmshtmltoolshttr2iBreakDowningredientsipredisobanditeratorsjomojquerylibjsonlitekernelshapKernSmoothknitrlabelinglaterlatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixmemoisemicemimeminqamitmlmodelenvnaniarnlmenloptrnnetnormnumDerivopensslordinalotelpanparallellyparsnippillarpkgconfigplyrprettyunitspROCprocessxprodlimprogressprogressrpromisespspurrrquartoR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadrrecipesreformulasrlangrmarkdownrpartrstudioapiS7sassscalesshapesparsevctrsSQUAREMstringistringrsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbucminfUpSetRutf8vctrsviridisviridisLitevisdatvroomwithrworkflowsxfunyamlyardstick

Survival Analysis with triageR
Overview | 1. Prepare the data | 2. Fit a survival model | 3. Validate the model | 4. Automated pipeline review | 5. Generate a TRIPOD+AI-aligned report | Summary
Rendered fromtriageR-survival-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-09-04
Started: 2026-09-04

Introduction to triageR
Overview | 1. Load and prepare data | 2. Check and handle missing data | 3. Fit a model | 4. Validate the model | Comparing engines | 5. Explain the model | 6. Automated pipeline review | 7. Sensitivity analysis | 8. Generate a TRIPOD+AI-aligned report | Optional: AI-assisted method recommendation | Summary
Rendered fromtriageR-intro.Rmdusingknitr::rmarkdown

Last update: 2026-08-05
Started: 2026-07-03

Case Study: Breast Cancer Diagnosis
Overview | 1. Load and prepare data | 2. Impute missing data | 3. Fit and compare three engines | 4. Explainability | 5. Automated pipeline review: interpreting the leakage flag | 6. Sensitivity analysis | Summary
Rendered fromtriageR-breast-cancer.Rmdusingknitr::rmarkdown

Last update: 2026-08-01
Started: 2026-07-03