savvyPR: Savvy Parity Regression Model Estimation with 'savvyPR'
Implements the Savvy Parity Regression 'savvyPR' methodology
for multivariate linear regression analysis. The package solves an
optimization problem that balances the contribution of each predictor
variable to ensure estimation stability in the presence of
multicollinearity. It supports two distinct parameterization methods,
a Budget-based approach that allocates a fixed loss contribution to
each predictor, and a Target-based approach (t-tuning) that utilizes
a relative elasticity weight for the response variable. The package
provides comprehensive tools for model estimation, risk distribution
analysis, and parameter tuning via cross-validation (PR1, PR2, and
PR3 model types) to optimize predictive accuracy. Methods are based
on Asimit, Chen, Ichim and Millossovich (2026)
<https://openaccess.city.ac.uk/id/eprint/35005/>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
glmnet, Matrix, stats, nleqslv, ggplot2, gridExtra |
| Suggests: |
MASS, knitr, rmarkdown, testthat (≥ 3.0.0), covr |
| Published: |
2026-03-17 |
| DOI: |
10.32614/CRAN.package.savvyPR |
| Author: |
Ziwei Chen  [aut,
cre],
Vali Asimit [aut],
Pietro Millossovich
[aut] |
| Maintainer: |
Ziwei Chen <Ziwei.Chen.3 at citystgeorges.ac.uk> |
| BugReports: |
https://github.com/ziwei-chenchen/savvyPR/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://ziwei-chenchen.github.io/savvyPR/ |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
savvyPR results |
Documentation:
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