psvr: Percentage-Error Support Vector Regression

Implements four support vector regression (SVR) models derived from a unified mathematical framework for percentage-error loss functions: epsilon-SVR minimizing the mean absolute percentage error (MAPE), its symmetric kernel extension, least-squares SVR (LS-SVR) minimizing the root mean square percentage error (RMSPE), and its symmetric counterpart. All models require strictly positive targets. The epsilon-SVR models are solved via a built-in sequential minimal optimization (SMO) algorithm (with 'osqp' available as an optional alternative backend) and the LS-SVR models via a linear system (base R). See Benavides-Herrera et al. (2026) doi:10.3390/math14101679 for the mathematical derivations.

See also

Useful links:

• https://pbenavidesh.github.io/psvr/

Author

Maintainer: Pablo Benavides-Herrera pbenavides@iteso.mx (ORCID)