Package: speakeasyR 0.1.4

speakeasyR: Fast and Robust Multi-Scale Graph Clustering

A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can be coerced into a matrix.

Authors:David Connell [aut, cre, cph], Chris Gaiteri [cph], Gábor Csárdi [cph, ctb], Tamás Nepusz [cph, ctb], Szabolcs Horvát [cph, ctb], Vincent Traag [cph, ctb], Fabio Zanini [cph, ctb], Daniel Noom [cph, ctb], The igraph development team [cph], Free Software Foundation, Inc. [cph], Ross Ihaka [cph, ctb], The R Development Core Team [cph], Royal Statistical Society [cph], The R Core Team [cph], The Regents of the University of California [cph], Timothy Davis [cph, ctb], Richard Lehoucq [cph, ctb], Danny Scrensen [cph, ctb], Phuong Vu [cph, ctb], Chao Yang [cph, ctb], Allan Cornet [cph, ctb], Sylvestre Ledru [cph, ctb], Chao Yang [cph, ctb], Rice University [cph], Scilab Enterprises [cph], Melissa O'Neill [cph, ctb], Steven Johnson [cph, ctb], Daniel G. [cph, ctb], Marc Stevens [cph, ctb], Minh Nguyen [cph, ctb], Elliot Paquette [cph, ctb], Pascal Pons [cph, ctb], Jordi Hermoso [cph, ctb], Sébastien Fabbro [cph, ctb], Shinya Tasaki [cph, ctb]