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        "author": "Mindstrong Health Data Science",
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        "description": "=======\nskl-PCA\n=======\n\nThis package implements Supervised Kernel-based Longitudinal Principal Components Analysis (skl-PCA) for predictor dimension reduction in longitudinal models. The software was written by members of the Mindstrong Health Data Science team:\n\n    * Patrick Staples, PhD\n    * Min Ouyang, PhD\n    * Bob Dougherty, PhD\n    * Greg Ryslik, PhD, FCAS, MAAA\n    * Paul Dagum, MD, PhD\n\n\nPlease contact us at `datascience@mindstronghealth.com <datascience@mindstronghealth.com>`_.\n\nNOTE: If you use this software in your work, please cite the following `paper <http://arxiv.org/abs/1808.06638>`_:\n\n    Patrick Staples, Min Ouyang, Robert F. Dougherty, Gregory A. Ryslik, and Paul Dagum (2018). Supervised Kernel PCA For Longitudinal Data. http://arxiv.org/abs/1808.06638.\n\nInstallation\n------------\n\nThe easiest way to install the package is via ``easy_install`` or ``pip``::\n\n    $ pip install sklPCA\n\nThis should also take care of the dependencies (numpy, scipy, pandas, and sklearn).\n\nUsage\n-----\n\nSee examples.py for examples of simulated data, predictor reduction, fitting, and cross-validated model performance.\n\n\nCopyright & License\n-------------------\n\nCopyright (c) 2018, `Mindstrong Health <http://mindstronghealth.com>`_. GNU Affero General Public License.\n\n\n\n",
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