psvr 0.1.0

Initial CRAN release.

psvr fits support vector regression models that optimise percentage-error losses directly, for problems where targets are strictly positive and relative accuracy matters more than absolute accuracy. Classical SVR minimises absolute-error losses, which weight a fixed error equally at every scale. The derivations are in Benavides-Herrera et al. (2026) doi:10.3390/math14101679.

• Two fitters: psvr_mape(), an epsilon-SVR with MAPE loss, and psvr_rmspe(), a least-squares SVR with RMSPE loss. On either, sym_type = "even" or "odd" selects the symmetric-kernel variant, giving four models in total. All require y > 0, which is validated at fit time.
• MAPE fits are solved by a built-in SMO algorithm implemented in C++, with the ‘osqp’ quadratic-programming backend available through solver = "osqp". RMSPE fits solve an augmented linear system in base R.
• make_kernel() builds RBF, linear and polynomial kernels, evaluated by compiled code; sigma_heuristic() supplies a median-distance bandwidth. User-written kernel closures are also accepted.
• predict(), fitted(), residuals(), coef(), summary() and print() methods for all four fit classes.
• tidymodels integration: six parsnip specifications — psvr_mape_rbf(), psvr_mape_poly(), psvr_mape_linear() and the three psvr_rmspe_*() counterparts — registered under the "psvr" engine, with sym_type as a tunable argument rather than a separate specification.
• dials parameters calibrated for percentage-error models, whose useful ranges differ from the absolute-error defaults: cost_psvr(), cost_psvr_ls_data(), margin_percentage(), rbf_sigma_psvr(), rbf_sigma_psvr_data() and sym_type_param(); plus psvr_option_add() and psvr_option_add_cost_ls() to apply data-driven ranges across a workflow set.
• psvr_cv() runs fold-wise MAPE fits, reusing the kernel matrix across folds and warm-starting each fold from the previous one.

Known limitation: on some problems the built-in SMO solver reaches max_iter without converging; it warns, and converged on the returned fit is FALSE. This was first observed with linear and polynomial kernels, but it also occurs with the RBF kernel, so the kernel does not determine the behaviour and no other predictor of it has been established. Check converged on the returned fit, and use solver = "osqp" where the SMO loop does not converge.

Relationship to the archived v0.0.2

This is the first CRAN release, but not the first published version. Version 0.0.2 is archived on Zenodo (doi:10.5281/zenodo.19935781) and is the version the accompanying Mathematics paper was computed against. The user-facing API has changed since then. Readers reproducing the paper should install the archived v0.0.2 rather than this release.