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Supervised Kernel-Based Longitudinal PCA (skl-PCA)

Project description

This 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:

  • Patrick Staples, PhD
  • Min Ouyang, PhD
  • Bob Dougherty, PhD
  • Greg Ryslik, PhD, FCAS, MAAA
  • Paul Dagum, MD, PhD

Please contact us at datascience@mindstronghealth.com.

NOTE: If you use this software in your work, please cite the following paper:

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.

Installation

The easiest way to install the package is via easy_install or pip:

$ pip install sklPCA

This should also take care of the dependencies (numpy, scipy, pandas, and sklearn).

Usage

See examples.py for examples of simulated data, predictor reduction, fitting, and cross-validated model performance.

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