We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.

Extends the functionality of the 'pROC' package for conducting smoothed receiver
  operating characteristic (ROC) curve analysis. Enables automated selection of the distribution
  families to be fit when smoothing ROC curves via the population probability density function
  estimation strategy described by Leeflang et al. (2008) <doi:10.1373/clinchem.2007.096032>,
  as well as generation of diagnostic performance and cutoff estimates from the resultant
  smoothed curves.

Provides point estimates and confidence intervals for receiver operating characteristic (ROC)–based diagnostic accuracy metrics for tests and biomarkers subject to verification bias. Supported metrics include the Area Under the ROC Curve (AUC), the Youden index, and the sensitivity at a user‑specified specificity level for two‑class continuous tests under missing‑at‑random (MAR) disease verification. Point estimation follows Alonzo and Pepe (2005) <doi:10.1111/j.1467-9876.2005.00477.x>. Multiple types of confidence intervals are implemented and compared, including bootstrap‑based, Method of Variance Estimates Recovery (MOVER)–based, and empirical likelihood (EL)–based intervals; see Wang et al. (2025) <doi:10.1177/09622802251322989> and <https://github.com/swang1021/rocvb>.

An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.

A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.