Release Name: GraphVar_beta_0.5

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.