Hierarchically AdaPtive (HAP) Analysis

Hierarchically AdaPtive (HAP) analysis is a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows for the extraction and quantification of features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data.

Screen Shot of HAP Analysis

Screen Shot of HAP Analysis


Uses:

HAP Analysis was developed for and applied to cortisol data. We anticipate that the methods could be applied to other hormones and other physiological signals.

Download:

To request a download of the application, email Dennis Dean at ddean@rics.bwh.harvard.edu.

Support:

NIH, NASA, and NSBRI

Copyright:

Copyright © [2002] The Brigham and Women's Hospital, Inc.
The Brigham and Women's Hospital, Inc. and its agents retain all rights to this software. The software is being made available without warranty of any kind, expressed or implied, including but not limited to implied warranties of merchantability OR fitness for a particular purpose. The Brigham and Women's Hospital, Inc. and its agents shall not be liable for any claims, liabilities, or losses relating to or arising from any use of this software.

Contact Information:

For any questions or issues regarding HAP Analysis software, please contact us here.


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