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hdqr - Fast Algorithm for Penalized Quantile Regression

Implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.

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2.48 score 1 dependents 142 downloads

fastkqr - A Fast Algorithm for Kernel Quantile Regression

Implements fast algorithms for kernel quantile regression and related models, including non-crossing kernel quantile regression and regularized linear quantile regression. The methods are described in Tang, Gu and Wang (2026) <doi:10.1080/10618600.2025.2541004>.

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openblasfortran

2.00 score 580 downloads

SAFEPG - A Novel SAFE Model for Predicting Climate-Related Extreme Losses

The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.

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2.00 score 499 downloads