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pye 0.1.0

• Initial Release: First official release of the pye package, providing a unified toolkit for high-dimensional binary classification, feature selection, and covariate adjustment.

New Implemented Methods

• Penalized Youden index Estimator (PYE): Introduced an embedded feature selection method for low- and high-dimensional binary classification (\(p \gg n\)) that directly maximizes a differentiable, Kernel-Smoothed (KS) version of the Youden Index using a standard normal CDF kernel.
• Covariate-adjusted Youden Index (covYI): Implemented an adaptive extension to incorporate covariates, allowing for observation-specific thresholding (\(t_i = c_i^\top \gamma\)) and automated covariate selection.

Optimization & Penalties

• Accelerated Proximal Gradient (APG): Implemented two efficient optimization algorithms tailored for non-convex and non-smooth objective functions: mmAPG (modified monotone variant) and mnmAPG (non-monotone variant).
• Sparsity-Inducing Penalties: Integrated closed-form proximal operators for a wide range of penalty functions, including \(L_{1/2}\) norm, \(L_1\) (Lasso), Elastic-Net, SCAD, and MCP.

Core Functionality & Benchmarking Suite

• Model Estimation: Added core routines pye_KS_estimation and covYI_KS_estimation to perform simultaneous feature selection and coefficient estimation.
• Unified Benchmarking: Included wrapper functions to estimate and compare established high-dimensional binary decision engines under identical data handling: Penalized Logistic Regression (plr_estimation), Penalized Support Vector Machines (psvm_estimation), and AUC-based methods (AucPR_estimation).

Tuning, Utilities & Simulations

• Hyperparameter Selection: Added automated \(k\)-fold cross-validation routines (pye_KS_compute_cv, plr_compute_cv, psvm_compute_cv, AucPR_compute_cv) to optimize tuning parameters (\(\lambda\) and \(\tau\)) across grid searches.
• Data Generation & Validation: Included create_sample_with_covariates to generate synthetic high-dimensional datasets with controlled correlation structures.
• Simulation Wrappers: Added pye_simulation_study and model_simulation_study to automate repeated train-test splits for evaluating selection stability and performance metrics under varying sparsity constraints.

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.
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