Metadata-Version: 1.2
Name: sparsereg
Version: 0.10.0
Summary: Modern sparse linear regression
Home-page: https://github.com/ohjeah/sparsereg
Author: Markus Quade
Author-email: info@markusqua.de
License: MIT
Description: sparsereg
        =========
        
        |travis| |pypi| |codecov| |zenodo|
        
        **sparsereg** is a collection of modern sparse (regularized) regression
        algorithms.
        
        Installation
        ------------
        
        ``pip install sparsereg``
        
        Citation
        --------
        
        If you use sparsereg please consider a citation:
        
        ::
        
            @misc{markus_quade_sparsereg,
              author       = {Markus Quade},
              title        = {sparsereg - collection of modern sparse regression algorithms},
              month        = feb,
              year         = 2018,
              doi          = {10.5281/zenodo.1173754},
              url          = {https://github.com/ohjeah/sparsereg}
            }
        
        Implemented algorithms
        ----------------------
        
        -  Mcconaghy, T. (2011). FFX: Fast, Scalable, Deterministic Symbolic
           Regression Technology. Genetic Programming Theory and Practice IX,
           235-260. `DOI:
           10.1007/978-1-4614-1770-5_13 <http://dx.doi.org/10.1007/978-1-4614-1770-5_13>`__
        -  Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz.
           “Discovering governing equations from data by sparse identification
           of nonlinear dynamical systems.” Proceedings of the National Academy
           of Sciences 113.15 (2016): 3932-3937. `DOI:
           10.1073/pnas.1517384113 <http://dx.doi.org/10.1073/pnas.1517384113>`__
        -  Bouchard, Kristofer E. “Bootstrapped Adaptive Threshold Selection for
           Statistical Model Selection and Estimation.” arXiv preprint
           arXiv:1505.03511 (2015).
        -  Ignacio Arnaldo, Una-May O’Reilly, and Kalyan Veeramachaneni.
           “Building Predictive Models via Feature Synthesis.” In Proceedings of
           the 2015 Annual Conference on Genetic and Evolutionary Computation
           (GECCO ’15), Sara Silva (Ed.). ACM, New York, NY, USA, 983-990. `DOI:
           10.1145/2739480.2754693 <http://dx.doi.org/10.1145/2739480.2754693>`__
        
        .. |travis| image:: https://travis-ci.org/Ohjeah/sparsereg.svg?branch=master
           :target: https://travis-ci.org/Ohjeah/sparsereg
        .. |pypi| image:: https://badge.fury.io/py/sparsereg.svg
           :target: https://badge.fury.io/py/sparsereg
        .. |codecov| image:: https://codecov.io/gh/Ohjeah/sparsereg/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/Ohjeah/sparsereg
        .. |zenodo| image:: https://zenodo.org/badge/80389199.svg
           :target: https://zenodo.org/badge/latestdoi/80389199
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
