pye package, providing a unified toolkit for
high-dimensional binary classification, feature selection, and covariate
adjustment.mmAPG (modified monotone
variant) and mnmAPG (non-monotone variant).pye_KS_estimation and covYI_KS_estimation to
perform simultaneous feature selection and coefficient estimation.plr_estimation), Penalized Support Vector Machines
(psvm_estimation), and AUC-based methods
(AucPR_estimation).pye_KS_compute_cv, plr_compute_cv,
psvm_compute_cv, AucPR_compute_cv) to optimize
tuning parameters (\(\lambda\) and
\(\tau\)) across grid searches.create_sample_with_covariates to generate synthetic
high-dimensional datasets with controlled correlation structures.pye_simulation_study and
model_simulation_study to automate repeated train-test
splits for evaluating selection stability and performance metrics under
varying sparsity constraints.
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