Metadata-Version: 1.1
Name: pdLSR
Version: 0.3.2
Summary: pdLSR: Pandas-aware least squares regression.
Home-page: https://github.com/mlgill/pdLSR
Author: Michelle Gill
Author-email: michelle@michellelynngill.com
License: BSD (3-clause)
Download-URL: https://github.com/mlgill/pdLSR
Description: pdLSR
        by Michelle L. Gill
        
        `pdLSR` is a library for performing least squares regression. It attempts to 
        seamlessly incorporate this task in a Pandas-focused workflow. Input data 
        are expected in dataframes, and multiple regressions can be performed using 
        functionality similar to Pandas `groupby`. Results are returned as grouped 
        dataframes and include best-fit parameters, statistics, residuals, and more. 
        
        `pdLSR` has been tested on python 2.7, 3.4, and 3.5. It requires Numpy, 
        Pandas, multiprocess (https://github.com/uqfoundation/multiprocess), and 
        lmfit (https://github.com/lmfit/lmfit-py). All dependencies are installable
        via pip or conda (see README.md).
        
        A demonstration notebook is provided in the `demo` directory or the demo
        can be run via GitHub (see README.md).
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
