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savvyPR  

Savvy Parity Regression Model Estimation with 'savvyPR'
View on CRAN: Click here


Download and install savvyPR package within the R console
Install from CRAN:
install.packages("savvyPR")

Install from Github:
library("remotes")
install_github("cran/savvyPR")

Install by package version:
library("remotes")
install_version("savvyPR", "0.1.0")



Attach the package and use:
library("savvyPR")
Maintained by
Ziwei Chen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-17
Latest Update: 2026-03-17
Description:
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/>.
How to cite:
Ziwei Chen (2026). savvyPR: Savvy Parity Regression Model Estimation with 'savvyPR'. R package version 0.1.0, https://cran.r-project.org/web/packages/savvyPR. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.1.0 (2026-03-17 20:10), 0.1.1 (2026-04-07 18:20)
Other packages that cited savvyPR R package
View savvyPR citation profile
Other R packages that savvyPR depends, imports, suggests or enhances
Complete documentation for savvyPR
Functions, R codes and Examples using the savvyPR R package
Full savvyPR package functions and examples
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