{ "_id": "6a1f34f3b401979e7342850f", "Package": "bikm1", "Type": "Package", "Title": "Co-Clustering Adjusted Rand Index and Bikm1 Procedure for\nContingency and Binary Data-Sets", "Version": "1.1.0", "Authors@R": "person(\"Valerie\", \"Robert\", email = \"valerie.robert.math@gmail.com\", role = c(\"aut\", \"cre\"))", "Maintainer": "Valerie Robert ", "Description": "Co-clustering of the rows and columns of a contingency or\nbinary matrix, or double binary matrices and model selection\nfor the number of row and column clusters. Three models are\nconsidered: the Poisson latent block model for contingency\nmatrix, the binary latent block model for binary matrix and a\nnew model we develop: the multiple latent block model for\ndouble binary matrices. A new procedure named bikm1 is\nimplemented to investigate more efficiently the grid of numbers\nof clusters. Then, the studied model selection criteria are the\nintegrated completed likelihood (ICL) and the Bayesian\nintegrated likelihood (BIC). Finally, the co-clustering\nadjusted Rand index (CARI) to measure agreement between\nco-clustering partitions is implemented. Robert Valerie,\nVasseur Yann, Brault Vincent (2021)\n.", "License": "GPL-2", "Encoding": "UTF-8", "RoxygenNote": "7.1.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-15 06:42:59 UTC", "User": "root" }, "Author": "Valerie Robert [aut, cre]", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2021-07-16 06:30:20 UTC", "RemoteUrl": "https://github.com/cran/bikm1", "RemoteRef": "HEAD", "RemoteSha": "4d4b6fb6a7c519ba66dafcbc89b64d2143405572", "MD5sum": "06c941aff20af1176ea1fc93d232d665", "_user": "cran", "_type": "src", "_file": "bikm1_1.1.0.tar.gz", "_fileid": "37ce735f2db3904377fa80ebf6a96fe9cfef59904a83ce674574967a533dc62b", "_filesize": 222887, "_sha256": "37ce735f2db3904377fa80ebf6a96fe9cfef59904a83ce674574967a533dc62b", "_created": "2026-05-15T06:42:59.000Z", "_published": "2026-06-02T19:54:27.781Z", "_distro": "noble", "_jobs": [ { "job": 79159381568, "time": 141, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7011628925" }, { 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"Imports" }, { "package": "methods", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "ade4", "role": "Imports" }, { "package": "pracma", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "reshape2", "role": "Imports" }, { "package": "grid", "role": "Imports" }, { "package": "lpSolve", "role": "Imports" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 1, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 250, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/bikm1" }, "_searchresults": 2, "_rbuild": "4.6.0", "_assets": [ "extra/bikm1.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, 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"Class \"BIKM1_LBM_Binary\"", "topics": [ "BIKM1_LBM_Binary-class" ] }, { "page": "BIKM1_LBM_Poisson-proc", "title": "BIKM1_LBM_Poisson fitting procedure", "topics": [ "BIKM1_LBM_Poisson" ] }, { "page": "BIKM1_LBM_Poisson-class", "title": "Class \"BIKM1_LBM_Poisson\"", "topics": [ "BIKM1_LBM_Poisson-class" ] }, { "page": "BIKM1_MLBM_Binary-proc", "title": "BIKM1_MLBM_Binary fitting procedure", "topics": [ "BIKM1_MLBM_Binary" ] }, { "page": "BIKM1_MLBM_Binary-class", "title": "Class \"BIKM1_MLBM_Binary\"", "topics": [ "BIKM1_MLBM_Binary-class" ] }, { "page": "BinBlocICL_LBM-proc", "title": "BinBlocICL_LBM function for computation of the ICL criterion in the Binary LBM", "topics": [ "BinBlocICL_LBM" ] }, { "page": "BinBlocICL_MLBM-proc", "title": "BinBlocICL_MLBM function for computation of the ICL criterion in the MLBM", "topics": [ "BinBlocICL_MLBM" ] }, { "page": "BinBlocRnd_LBM-proc", "title": "BinBlocRnd_LBM function for binary data matrix simulation", "topics": [ "BinBlocRnd_LBM" ] }, { "page": "BinBlocRnd_MLBM-proc", "title": "BinBlocRnd_MLBM function for binary double data matrix simulation", "topics": [ "BinBlocRnd_MLBM" ] }, { "page": "BinBlocVisu_LBM-proc", "title": "BinBlocVisu_LBM function for visualization of binary matrix datasets", "topics": [ "BinBlocVisu_LBM" ] }, { "page": "BinBlocVisu_MLBM-proc", "title": "BinBlocVisu_MLBM function for visualization of double matrix datasets", "topics": [ "BinBlocVisu_MLBM" ] }, { "page": "BinBlocVisuResum_LBM-proc", "title": "BinBlocVisuResum_LBM function for visualization of binary matrix data-sets", "topics": [ "BinBlocVisuResum_LBM" ] }, { "page": "BinBlocVisuResum_MLBM-proc", "title": "BinBlocVisuResum_MLBM function for visualization of double matrix datasets", "topics": [ "BinBlocVisuResum_MLBM" ] }, { "page": "CARI-proc", "title": "CARI function for agreement between co-clustering partitions", "topics": [ "CARI" ] }, { "page": "CE_LBM-proc", "title": "CE_LBM function for agreement between co-clustering partitions", "topics": [ "CE_LBM" ] }, { "page": "CE_MLBM-proc", "title": "CE_MLBM function for agreement between co-clustering partitions in the MBLM", "topics": [ "CE_MLBM" ] }, { "page": "CE_simple-proc", "title": "CE_simple function for agreement between clustering partitions", "topics": [ "CE_simple" ] }, { "page": "CoNMI-proc", "title": "CoNMI function for agreement between co-clustering partitions", "topics": [ "CoNMI" ] }, { "page": "ENMI-proc", "title": "ENMI function for agreement between co-clustering partitions", "topics": [ "ENMI" ] }, { "page": "MI_simple-proc", "title": "MI_simple function for agreement between two partitions", "topics": [ "MI_simple" ] }, { "page": "NCE_LBM-proc", "title": "NCE_LBM function for agreement between co-clustering partitions using NCE_simple", "topics": [ "NCE_LBM" ] }, { "page": "NCE_simple-proc", "title": "NCE_simple function for agreement between clustering partitions", "topics": [ "NCE_simple" ] }, { "page": "plot-BIKM1_LBM_Binary-method", "title": "Plot method for a 'BIKM1_LBM_Binary' object", "topics": [ "plot,BIKM1_LBM_Binary-method" ] }, { "page": "plot-BIKM1_LBM_Poisson-method", "title": "Plot method for a 'BIKM1_LBM_Poisson' object", "topics": [ "plot,BIKM1_LBM_Poisson-method" ] }, { "page": "plot-BIKM1_MLBM_Binary-method", "title": "Plot method for a 'BIKM1_MLBM_Binary' object", "topics": [ "plot,BIKM1_MLBM_Binary-method" ] }, { "page": "PoissonBlocBIC-proc", "title": "PoissonBlocBIC function for the computation of the BIC criterion in the Poisson LBM", "topics": [ "PoissonBlocBIC" ] }, { "page": "PoissonBlocICL-proc", "title": "PoissonBlocICL function for the computation of the ICL criterion in the Poisson LBM", "topics": [ "PoissonBlocICL" ] }, { "page": "PoissonBlocRnd-proc", "title": "PoissonBlocRnd function for contingency data simulation", "topics": [ "PoissonBlocRnd" ] }, { "page": "PoissonBlocVisu-proc", "title": "PoissonBlocVisu function for visualization of contingency 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