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  "Title": "Savvy Parity Regression Model Estimation with 'savvyPR'",
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  "Description": "Implements the Savvy Parity Regression 'savvyPR'\nmethodology for multivariate linear regression analysis. The\npackage solves an optimization problem that balances the\ncontribution of each predictor variable to ensure estimation\nstability in the presence of multicollinearity. It supports two\ndistinct parameterization methods, a Budget-based approach that\nallocates a fixed loss contribution to each predictor, and a\nTarget-based approach (t-tuning) that utilizes a relative\nelasticity weight for the response variable. The package\nprovides comprehensive tools for model estimation, risk\ndistribution analysis, and parameter tuning via\ncross-validation (PR1, PR2, and PR3 model types) to optimize\npredictive accuracy. Methods are based on Asimit, Chen, Ichim\nand Millossovich (2026)\n<https://openaccess.city.ac.uk/id/eprint/35005/>.",
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  "Date/Publication": "2026-03-18 17:11:33 UTC",
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