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:
speakeasyR_0.1.4.tar.gz
speakeasyR_0.1.4.tar.gz(r-4.5-noble)speakeasyR_0.1.4.tar.gz(r-4.4-noble)
speakeasyR_0.1.4.tgz(r-4.4-emscripten)speakeasyR_0.1.4.tgz(r-4.3-emscripten)
speakeasyR.pdf |speakeasyR.html✨
speakeasyR/json (API)
NEWS
# Install 'speakeasyR' in R: |
install.packages('speakeasyR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/speakeasy-2/speakeasyr/issues
Last updated 3 months agofrom:9265a2a477. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 24 2024 |
R-4.5-linux-x86_64 | OK | Nov 24 2024 |
Readme and manuals
Help Manual
Help page | Topics |
---|---|
SpeakEasy 2 community detection | cluster |
Cluster a gene expression matrix | cluster_genes |
K-nearest neighbors graph | knn_graph |
Group nodes by community | order_nodes |