Authors@R: c(person(given="Jamaal", family="Green", role = c("cre","aut"), email="jamaal.green@gmail.com"),
person(given="Liming", family="Wang", role =c("aut"), email="lmwang@pdx.edu"),
person(given="Dillon", family="Mahmoudi", role =c("aut"), email="dillonm@umbc.edu"),
person(given="Matthew",family="Rogers", role=c("ctb"), email = "matthew.rogers09@gmail.com"))
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, devtools, pacman
Packaged: 2022-02-03 23:46:03 UTC; jamaalgreen

Title: Implementation of the 'Python leidenalg' Module
Description: Implements the 'Python leidenalg' module to be called in R.
Enables clustering using the leiden algorithm for partition a graph into communities.
See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg>
Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
Suggests: covr, testthat, spelling, knitr, rmarkdown, igraph,

Title: R Implementation of Leiden Clustering Algorithm
Authors@R: c(person("S. Thomas", "Kelly", email = "tom.kelly@riken.jp", role = c("aut", "cre", "trl")),
person("Vincent A.", "Traag", email = "v.a.traag@cwts.leidenuniv.nl", role = c("com")))
Imports: methods, reticulate, Matrix, igraph
Suggests: data.table, tibble, devtools, covr, testthat, spelling,
Author: S. Thomas Kelly [aut, cre, trl],

Authors@R: c(person("S. Thomas", "Kelly", email = "tomkellygenetics@gmail.com", role = c("aut", "cre", "trl")),
Suggests: bipartite, covr, data.table, devtools, graphsim, knitr,
multiplex, multinet, markdown, network, RColorBrewer,

markdown, RColorBrewer, rmarkdown, spelling, testthat, tibble

multiplex, multinet, multinets, mully, markdown, network,
RColorBrewer, rmarkdown, spelling, testthat, tibble

Imports: methods, reticulate, Matrix, igraph (>= 1.2.7)
markdown, multiplex, multinet, network, qpdf, RColorBrewer,
remotes, rmarkdown, spelling, testthat, tibble

markdown, multiplex, multinet, network, RColorBrewer, remotes,

Title: Implements the Leiden Algorithm via an R Interface
Authors@R: c(person("Peter", "Kharchenko", email = "Peter_Kharchenko@hms.harvard.edu", role = c("aut")), person("Viktor", "Petukhov", email = "viktor.s.petukhov@ya.ru", role = c("aut")), person("V.A.", "Traag", email = "v.a.traag@cwts.leidenuniv.nl", role = c("ctb")), person("Gábor", "Csárdi", email = "sardi.gabor@gmail.com", role = c("ctb")), person("Tamás", "Nepusz", email = "ntamas@gmail.com", role = c("ctb")), person("Minh Van", "Nguyen", email = "nguyenminh2@gmail.com", role = c("ctb")), person("Evan", "Biederstedt", email = "evan.biederstedt@gmail.com", role=c("cre", "aut")))
Description: An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.
Copyright: See the file COPYRIGHTS for various leidenAlg copyright
Imports: graphics, grDevices, Matrix.utils, parallel, Rcpp (>= 1.0.5),
Packaged: 2020-11-06 17:47:55 UTC; evanbiederstedt