Automated Machine Learning and AI Agent Tools for Clinical Prediction Modelling

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Documentation for package ‘triageR’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

tr_agent_review	Review a clinical modelling pipeline for common pitfalls
tr_check_missing	Check missing data in a clinical dataset
tr_explain	Explain a clinical prediction model
tr_fit	Fit a clinical prediction model
tr_fit_survival	Fits a survival model using the 'censored'/'parsnip' framework. The outcome must be specified as separate time and event columns (following 'survival::Surv()' convention), and the user must explicitly choose an engine.
tr_impute	Impute missing values in a clinical dataset
tr_launch_app	Launch the triageR Shiny app
tr_load_clinical	Load and standardize a clinical dataset
tr_recommend_method	Recommend an appropriate statistical or ML method (AI agent)
tr_sensitivity	Run an automated sensitivity analysis battery
tr_tripod_report	Generate a TRIPOD+AI-aligned clinical model report
tr_validate	Validate a clinical prediction model
tr_validate_survival	Validate a clinical survival model