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LICENSE.txt
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Non-parametric Dynamic Functional Connectivity Modeling - Software License Agreement
Non-parametric Dynamic Functional Connectivity Modeling Copyright Notice
(2016) Technical University of Denmark (hereinafter reffered to as "DTU").
All rights reserved. Contact information sfvn at dtu dot dk
Article 1 - Definitions
1. The "Software" constitutes any software distributed as part of or pertaining
to the Non-parametric Dynamic Functional Connectivity Modeling as made
available online at https://brainconnectivity.compute.dtu.dk/
or https://github.com/sfvnDTU/ndfc .
2. "Academic" and "non-profit" use shall mean a user of Software: who is employed
by, or a student enrolled at, or a scientist legitimately affiliated with an
academic, non-profit or government institution; and whose use of the
Software is on behalf of and in the interest of such academic, non-profit
or government institution and is not on behalf of a commercial entity.
Article 2 - License
Redistribution and use in source and binary forms, with or without
modification, are permitted for academic and non-profit use provided
that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. As long as you qualify as an Academic User, DTU hereby grants free access
to the Software supplied by DTU for non-commercial use only.
You acknowledge and agree that you may not use the Software for
commercial purpose without first obtaining a commercial license from DTU.
4. Neither the names of the contributors nor of their affiliated
institutions may be used to endorse or promote products derived from this
software without specific prior written permission.
5. WARRANTY DISCLAIMER. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS
AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,
BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSE<QUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
**************************************************************************
******************* Licenses for third party toolboxes *******************
**************************************************************************
****** The Infinite Hidden Markov Model 0.5 ******
The following copyright notice has been copied from the IHMM implementation
made by Juergen Van Gael (http://mloss.org/software/view/205/)
(C) Copyright 2008-2009, Jurgen Van Gael (jv279 -at- cam .dot. ac .dot. uk)
http://mlg.eng.cam.ac.uk/jurgen/
Permission is granted for anyone to copy, use, or modify these programs
and accompanying documents for purposes of research or education,
provided this copyright notice is retained, and note is made of
any changes that have been made.
These programs and documents are distributed without any warranty,
express or implied. As the programs were written for research purposes only,
they have not been tested to the degree that would be advisable in any
important application.
All use of these programs is entirely at the user's own risk.
** CHANGES FROM ORIGINAL CODE **
The following MATLAB functions have been directly copied to the current project
dirichlet_sample.m
SampleTransitionMatrix.m
In our main Gibbs sweep (in IHMMgibbs.m) we have used a code snippet from
iHmmSampleGibbs.m that calculates Dirichlet Process contribution
to the conditional density.
It has been indicated with comments in the code where this snippet appears.
The iHmmHypersample.m function has been modified in the input, such that
the transition matrix is pre-calculated and now passed in as an argument
instead of the state-sequence.
****** mvgammaln.m ******
The multivariate log-gamma function was implemented by Dahua Lin ()