The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

pye: Penalized Youden Index Estimator R Package

The pye package implements the penalized variable selection and classification methodologies developed during my PhD research at the Complutense University of Madrid (UCM).

The package features the Penalized Youden Index Estimator (PYE) and the Covariate-adjusted Youden Index (covYI) estimator, designed for simultaneous feature selection, coefficient estimation, and threshold optimization, both in low- and high-dimensional settings.

📌 Repository Structure & Versions

• main (Version 0.1.0 - 2026): Clean production-ready codebase optimized according to CRAN guidelines. Prepared for submission.

⚙️ Core Features Included

• Primary Estimators: Smooth kernel-based estimation for standard/weighted PYE and covYI.
• Sparsity Penalties: Full implementation of \(L_{1/2}\), \(L_1\) (Lasso), Elastic-Net, SCAD, and MCP regularization.
• Optimization Engines: Modified monotone (mmAPG) and non-monotone (mnmAPG) Accelerated Proximal Gradient solvers.
• Cross-Validation & Simulations: Integrated automated grid search for hyperparameter tuning (\(\lambda\) and \(\tau\)) and parallelized train-test validation splits.
• Benchmark Modules: Standardized interfaces for penalized logistic regression, penalized Support Vector Machines (SVM), and penalized AUC-based regression.

🚀 Installation (for Advisors & Collaborators)

To install the package directly from this private repository, make sure you have devtools installed and run the following command in R/RStudio:

```R if (!require(“devtools”)) install.packages(“devtools”) devtools::install_github(“UCMpyePackage/pye”)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.