Spatiotemporal modeling tools for Python

This package provides tools for modeling and analyzing spatial and temporal autocorrelation in Python. It is based on the methods from the paper Functional brain networks reflect spatial and temporal autocorrelation. Included are methods to compute the following statistics:

• Compute TA-Δ₁ (i.e. first-order temporal autocorrelation)
• Compute SA-λ and SA-∞ (i.e. measurements of spatial autocorrelation)
• Lin's concordance
• Fingerprinting performance

It will also generate surrogate timeseries for the following:

• Spatiotemporal model
• Intrinsic timescale + SA model
• Zalesky matching model
• Eigensurrogate model
• Phase randomization null model

Other great packages

This package does NOT provide the following methods from the paper, which are readily available in these other great packages:

• Graph theoretical measures can be computed with bctpy
• Intraclass Correlation Coefficient (ICC) can be computed using pingouin.intraclass_corr
• Partial correlation can be computed using pingouin.partial_corr
• Plotting on the surface of the brain can be accomplished with wbplot
• Layout of the figures was with CanD