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.