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        "description": "\n# pyPhenology  \npyPheonology is a software package for building plant phenology models. It has\nnumpy at it\u2019s core, making model building and prediction extremely fast. The\ncore code was written to model phenology observations from the National\nPhenology Network, where abundant species have several thousand observations.\nThe API is inspired by scikit-learn, so all models can work interchangeably\nwith the same code. pyPhenology is currently used to build the continental\nscale phenology forecasts on http://phenology.naturecast.org\n\n## Full documentation  \n\n[http://pyphenology.readthedocs.io/en/master/](http://pyphenology.readthedocs.io/en/master/)\n\n\n## Installation\nRequires: scipy, pandas, joblib, and numpy\n\nInstall via pip\n\n```\npip install pyPhenology\n```\n\nOr install the latest version from Github  \n\n```\npip install git+git://github.com/sdtaylor/pyPhenology\n```\n\n## Get in touch\nSee the [GitHub Repo](https://github.com/sdtaylor/pyPhenology) to see the \nsource code or submit issues and feature requests.\n\n## Citation\n\nIf you use this software in your research please cite it as:\n\nTaylor, S. D. (2018). pyPhenology: A python framework for plant phenology \nmodelling. Journal of Open Source Software, 3(28), 827. https://doi.org/10.21105/joss.00827\n\nBibtex:\n```\n@article{Taylor2018,\nauthor = {Taylor, Shawn David},\ndoi = {10.21105/joss.00827},\njournal = {Journal of Open Source Software},\nmendeley-groups = {Software/Data},\nmonth = {aug},\nnumber = {28},\npages = {827},\ntitle = {{pyPhenology: A python framework for plant phenology modelling}},\nurl = {http://joss.theoj.org/papers/10.21105/joss.00827},\nvolume = {3},\nyear = {2018}\n}\n\n```\n\n## Acknowledgments\n\nDevelopment of this software was funded by\n[the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative](http://www.moore.org/programs/science/data-driven-discovery) through\n[Grant GBMF4563](http://www.moore.org/grants/list/GBMF4563) to Ethan P. White.\n\n\n\n",
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