Changes in version 0.1.0 (2026-06-04)                  

  - 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.