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The default network\nreconstruction method we use is based on Pearson correlation.\nHowever, lionessR can run on any network reconstruction\nalgorithms that returns a complete, weighted adjacency matrix.\nlionessR works for both unipartite and bipartite networks.", "Authors@R": "c(person(c(\"Marieke\", \"Lydia\"), \"Kuijjer\", \nrole = c(\"aut\"),\nemail = \"marieke.kuijjer@ncmm.uio.no\",\ncomment = c(ORCID = \"0000-0001-6280-3130\")),\nperson(\"Ping-Han\", \"Hsieh\",\nrole = c(\"cre\"),\nemail = \"dn070017@gmail.com\",\ncomment = c(ORCID = \"0000-0003-3054-1409\")))", "License": "MIT + file LICENSE", "LazyData": "TRUE", "biocViews": "Network, NetworkInference, GeneExpression", "BugReports": "https://github.com/mararie/lionessR/issues", "URL": "https://github.com/mararie/lionessR", "Encoding": "UTF-8", "RoxygenNote": "6.0.1", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:50:59 UTC", 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