The fastest and most accurate methods for quantile regression, now in python.
Free software: MIT license
Documentation: https://mhoward2718.github.io/pinball-qr/
Features
sklearn-compatible API — QuantileRegressor works with pipelines, cross-validation, and all the usual sklearn tooling.
Multiple solvers — Barrodale-Roberts simplex (br), Frisch-Newton interior point (fn/fnb), preprocessed Frisch-Newton (pfn), LASSO-penalised (lasso), and ADMM via POGS (pogs).
Statistical inference — IID, NID, and kernel standard errors, rank inversion confidence intervals, and bootstrap methods.
Nonparametric quantile regression — optimal quantization via Competitive Learning Vector Quantization (CLVQ).
Credits & Acknowledgements
Pinball-QR is authored by Michael Howard.
This project builds on the work of several researchers and open-source projects:
- quantreg (R package)
The Fortran solvers (rqbr, rqfn, rqfnb) are ported from the quantreg R package by Roger Koenker, with contributions from Stephen Portnoy, Pin Tian Ng, Blaise Melly, Achim Zeileis, and others. See Koenker, R. (2005) Quantile Regression, Cambridge University Press.
- POGS (Proximal Operator Graph Solver)
The ADMM-based solver uses C++ source from POGS by Christopher Fougner (MIT license). See Fougner & Boyd (2018), Parameter Selection and Pre-Conditioning for a Graph Form Solver, in Emerging Applications of Control and Systems Theory, Springer.
- QuantifQuantile (R package)
The nonparametric quantile regression via optimal quantization is based on the QuantifQuantile R package by Isabelle Charlier, Davy Paindaveine, and Jérôme Saracco. See Charlier, Paindaveine & Saracco (2015), Estimation of Conditional Quantiles using Optimal Quantization.
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Metadata
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Release files for pinball-qr 1.0.0
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