Metadata-Version: 2.1
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
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
Requires-Dist: scikit-learn[alldeps] (>=0.19)
Requires-Dist: numpy
Requires-Dist: joblib
Requires-Dist: toolz
Requires-Dist: sympy

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


