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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).
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Release files for pdLSR 0.3.6
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