Python implementation of Friedman's Supersmoother
Project description
This is an efficient implementation of Friedman’s SuperSmoother [1] algorithm in pure Python. It makes use of [numpy](http://numpy.org) for fast numerical computation.
Installation
Installation is simple: download the source code and type ` $ python setup.py install `
Testing
This code has full unit tests implemented in [nose](https://nose.readthedocs.org/en/latest/). With nose installed, you can run the test suite using ` $ nosetests supersmoother `
[1]: Friedman, J. H. (1984) A variable span scatterplot smoother. Laboratory for Computational Statistics, Stanford University Technical Report No. 5. ([pdf](http://www.slac.stanford.edu/cgi-wrap/getdoc/slac-pub-3477.pdf))
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