Software, Codes, & Systems
Research Software
Open-source research packages implementing published or in-progress methodology. Released first.DigplexQ Research DigplexQ is an open-source Python package for performing computations on digraph-based complexes, including directed flag complexes and path complexes. It is designed as an adjacency-matrix-centered package, allowing users to perform all computations directly from an adjacency matrix without requiring explicit construction of the underlying complex. The package implements a range of quantitative methods for analyzing digraph-based complexes, primarily based on concepts from directed Q-analysis. At present, the implementation focuses exclusively on lower q-adjacency.
PyPDC Research PyPDC is an open-source Python package for estimating Partial Directed Coherence (PDC) and Directed Coherence (DC) from multivariate time-series data, with a particular focus on brain connectivity analysis. It implements asymptotic PDC estimation, allowing researchers to characterize directional information flow between pairs of brain regions or recording channels in the frequency domain.
PyTropical Research PyTropical is an open-source Python package for tropical mathematics, providing computational tools for working with tropical algebra. The package currently focuses on max-plus and min-plus algebras, implementing fundamental tropical operations such as tropical addition, multiplication, and exponentiation.
TemporalHypermotifs Research Detection, classification, and quantification of temporal hypergraph motifs (THMs). Implements measures at motif level, single-layer hypergraph level, and multilayer hypergraph level.
GeometrySpectralEvo ResearchIn development Earlier-stage package - This package implements methods to treat the empirical spectral density (ESD) of a normalized graph Laplacian as a point in Wasserstein space, and a temporal graph as a curve in this space. It also implements geometric descriptors to analyze the geometry of graph spectral evolution over time.
Notes and Notebooks
Notes and exploratory codes (not packaged for reuse).Notes on Topological Deep Learning Research Jupyter notebooks implementing neural network models for classifying random graphs and hypergraphs based on their underlying generative models.
Notes on Artificial Intelligence in Neuroimaging Analysis Research A collection of Jupyter notebooks providing algorithms and deep learning models for the analysis of fMRI, EEG, and MEG data.