Package: pye 0.1.0

Claudio J. Salaroli

pye: Penalized Youden Index Estimator

Implements the Penalized Youden Index Estimator (PYE) and the Covariate-Adjusted Youden Index Estimator (covYI), providing a novel framework for feature and covariate selection and combination in high-dimensional binary classification problems. Methodologies are based on Salaroli and Pardo (2023) <doi:10.1016/j.chemolab.2023.104786> and an unpublished manuscript by Salaroli and Pardo (2026) under review.

Authors:Claudio J. Salaroli [aut, cre], Maria del Carmen Pardo [aut]

pye_0.1.0.tar.gz
pye_0.1.0.tar.gz(r-4.7-arm64)pye_0.1.0.tar.gz(r-4.7-x86_64)pye_0.1.0.tar.gz(r-4.6-arm64)pye_0.1.0.tar.gz(r-4.6-x86_64)
pye_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pye/json (API)
NEWS

# Install 'pye' in R:
install.packages('pye', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 32 exports 62 dependencies

Last updated from:846f3ebafc. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK223
linux-devel-x86_64OK236
source / vignettesOK593
linux-release-arm64OK199
linux-release-x86_64OK247
wasm-releaseOK142

Exports:AucPR_compute_cvAucPR_estimationAucPR_predictcalibrate_lambda_maxcalibrate_lambda_mincovYI_KScovYI_KS_estimationcreate_data_allcreate_lambdacreate_samplecreate_sample_with_covariatesMCP_functionmmAPGmnmAPGmodel_simulation_studyplr_compute_cvplr_estimationplr_predictproximal_operator_ENproximal_operator_L1proximal_operator_L12proximal_operator_MCPproximal_operator_SCADpsvm_compute_cvpsvm_estimationpsvm_predictpye_KSpye_KS_compute_cvpye_KS_estimationpye_KS_simulation_studySCAD_functionscaling_df_for_pye

Dependencies:bootclasscliclustercodetoolscorpcorcpp11crscubaturee1071evmixfarverforeachggplot2glmnetgluegmpgslgtableisobanditeratorslabelinglatticelifecyclemaptreeMASSMatrixMatrixModelsmlegpmomentsncvregnor1mixnpOptimalCutpointspbivnormpenalizedSVMplyrpROCproxyquadprogquantregR6RColorBrewerRcppRcppEigenrlangRmpfrROCnRegrpartS7scalesshapeSparseMsparseSVMspatstat.univarspatstat.utilsstatmodsurvivaltgpvctrsviridisLitewithr

Introduction to pye and covYI estimators

Rendered frompye_intro.Rmdusingknitr::rmarkdownon Jun 04 2026.

Last update: 2026-06-04
Started: 2026-06-04

Readme and manuals

Help Manual

Help pageTopics
Cross-Validation for Optimal AucPR Regularization Parameter SelectionAucPR_compute_cv
AucPR Estimation for Coefficient and Feature SelectionAucPR_estimation
Prediction and Performance Evaluation for Penalized AucPR ModelsAucPR_predict
Calibrate Maximum Value for the Penalty Parametercalibrate_lambda_max
Calibrate Minimum Value for the Penalty Parametercalibrate_lambda_min
Covariate-Adjusted Youden Index (covYI) method with Kernel Smoothing Estimation of the Density FunctionscovYI_KS
Estimation of Optimal Covariate Coefficients through Penalized Covariate-Adjusted Youden IndexcovYI_KS_estimation
Generate Correlated Regressors and Covariatescreate_data_all
Generate a Sequence of Penalty Parameters (lambda)create_lambda
Create Synthetic High-Dimensional Sample with Binary Targetcreate_sample
Create a Synthetic Data Set with Covariates and Binary Targetcreate_sample_with_covariates
Minimax Concave Penalty (MCP) Function ValueMCP_function
Monotone Accelerated Proximal Gradient (APG) methodmmAPG
Non-Monotone Accelerated Proximal Gradient (APG) methodmnmAPG
Simulation Study for Penalized Models on Real Datamodel_simulation_study
Cross-Validation for Optimal glmnet Regularization Parameter Selectionplr_compute_cv
glmnet Estimation for Coefficient and Feature Selectionplr_estimation
Prediction and Performance Evaluation for Penalized glmnet Modelsplr_predict
Proximal Operator for the Elastic-Net Penaltyproximal_operator_EN
Proximal Operator for the Convex \textrm{L}_{1} (Lasso) Penaltyproximal_operator_L1
Proximal Operator for the Non-Convex \textrm{L}_{1 / 2} Penaltyproximal_operator_L12
Proximal Operator for the Non-Convex MCP Penaltyproximal_operator_MCP
Proximal Operator for the Non-Convex SCAD Penaltyproximal_operator_SCAD
Cross-Validation for Optimal Penalized svm Regularization Parameter Selectionpsvm_compute_cv
Penalized svm Estimation for Coefficient and Feature Selectionpsvm_estimation
Prediction and Performance Evaluation for Penalized svm Modelspsvm_predict
Penalized Youden Index (pye) function via Kernel Smoothing Densitypye_KS
Cross-Validation for Optimal pye KS Regularization Parameter Selectionpye_KS_compute_cv
pye KS Estimation for Coefficient and Feature Selectionpye_KS_estimation
Simulation Study for Penalized Youden Index (pye) on Real Datapye_KS_simulation_study
Smoothly Clipped Absolute Deviation (SCAD) Function ValueSCAD_function
Center and Scale Continuous Regressors for pye Modelingscaling_df_for_pye