Metadata-Version: 1.1
Name: loess
Version: 2.0.11
Summary: LOESS: Local Regression Smoothing in One or Two Dimensions
Home-page: http://purl.org/cappellari/software
Author: Michele Cappellari
Author-email: michele.cappellari@physics.ox.ac.uk
License: Other/Proprietary License
Description: The LOESS Package
        =================
        
        **Local Regression Smoothing in One or Two Dimensions**
        
        .. image:: https://img.shields.io/pypi/v/loess.svg
                :target: https://pypi.org/project/loess/
        .. image:: https://img.shields.io/badge/arXiv-1208.3523-orange.svg
            :target: https://arxiv.org/abs/1208.3523
        .. image:: https://img.shields.io/badge/DOI-10.1093/mnras/stt644-blue.svg
                :target: https://doi.org/10.1093/mnras/stt644
        
        LOESS is a Python implementation of the Local Regression Smoothing method of
        `Cleveland (1979) <http://www.jstor.org/stable/2286407>`_ (in 1-dim) and
        `Cleveland & Devlin (1988) <http://www.jstor.org/stable/2289282>`_ (in 2-dim).
        
        Attribution
        -----------
        
        If you use this software for your research, please cite the LOESS package of
        `Cappellari et al. (2013b) <http://adsabs.harvard.edu/abs/2013MNRAS.432.1862C>`_,
        where the implementation was described. The BibTeX entry for the paper is::
        
            @ARTICLE{Cappellari2013b,
                author = {{Cappellari}, M. and {McDermid}, R.~M. and {Alatalo}, K. and 
                    {Blitz}, L. and {Bois}, M. and {Bournaud}, F. and {Bureau}, M. and 
                    {Crocker}, A.~F. and {Davies}, R.~L. and {Davis}, T.~A. and 
                    {de Zeeuw}, P.~T. and {Duc}, P.-A. and {Emsellem}, E. and {Khochfar}, S. and 
                    {Krajnovi{\'c}}, D. and {Kuntschner}, H. and {Morganti}, R. and 
                    {Naab}, T. and {Oosterloo}, T. and {Sarzi}, M. and {Scott}, N. and 
                    {Serra}, P. and {Weijmans}, A.-M. and {Young}, L.~M.},
                title = "{The ATLAS$^{3D}$ project - XX. Mass-size and mass-{$\sigma$}
                    distributions of early-type galaxies: bulge fraction drives kinematics,
                    mass-to-light ratio, molecular gas fraction and stellar initial mass
                    function}",
                journal = {MNRAS},
                eprint = {1208.3523},
                 year = 2013,
                volume = 432,
                pages = {1862-1893},
                  doi = {10.1093/mnras/stt644}
            }
        
        Installation
        ------------
        
        install with::
        
            pip install loess
        
        Without writing access to the global ``site-packages`` directory, use::
        
            pip install --user loess
        
        Documentation
        -------------
        
        See ``loess/examples`` and the files headers.
        
        License
        -------
        
        Copyright (c) 2010-2018 Michele Cappellari
        
        This software is provided as is without any warranty whatsoever.
        Permission to use, for non-commercial purposes is granted.
        Permission to modify for personal or internal use is granted,
        provided this copyright and disclaimer are included in all
        copies of the software. All other rights are reserved.
        In particular, redistribution of the code is not allowed.
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
