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pdLSR: Pandas-aware least squares regression.

Project 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).

Release History

Release History

This version
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0.3.6

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0.3.5

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0.3.3

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0.3.2

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0.3.1

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0.3

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0.2.7

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0.2.6

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0.2.5

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pdLSR-0.3.6.tar.gz (336.2 kB) Copy SHA256 Checksum SHA256 Source Jul 24, 2016

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