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Notes:
UPDATES GraphVar beta 0.50
1. Implementation of sparse inverse covariance estimation (SICE)
with the graphical lasso also known as Gaussian graphical models
- Generate covariance matrices from time courses (should be used
with SICE threshold (network construction)
- Network construction: will use SICE to produce binary matrices
with selected target densities using the input covariance matrices
- Generate connectivity matrices: directly produces binary networks
with a specific target density based on SICE from input timecourses
(saved in SICEMatrix folder)
2. Implementation of a new way of generating random time-series:
- Multivariate algorithm from Prichard, D., & Theiler, J. (1994).
Generating surrogate data for time series with several
simultaneously measured variables.
Physical Review Letters, 73(7), 951.
- Basically works like this: will cause randomizing the observed
time series by taking its Fourier transform, scrambling its phase
and then inverting the transform.
Changes:
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