{ "_id": "6abac42599f7aa8c6aa4265e", "Encoding": "UTF-8", "Package": "clusterMI", "Type": "Package", "Title": "Cluster Analysis with Missing Values by Multiple Imputation", "Version": "1.6", "Date": "2026-04-03", "Authors@R": "c(\nperson(\"Vincent\",\"Audigier\",email=\"vincent.audigier@cnam.fr\",role=c(\"aut\",\"cre\"),comment=\"CNAM MSDMA team\"),\nperson(\"Hang Joon\",\"Kim\",email=\"hang.kim@uc.edu\",role=c(\"ctb\"),comment=\"University of Cincinnati\"))", "Description": "Allows clustering of incomplete observations by addressing\nmissing values using multiple imputation. For achieving this\ngoal, the methodology consists in three steps, following\nAudigier and Niang 2022 . I)\nMissing data imputation using dedicated models. Four multiple\nimputation methods are proposed, two are based on joint\nmodelling and two are fully sequential methods, as discussed in\nAudigier et al. (2021) . II)\ncluster analysis of imputed data sets. Six clustering methods\nare available (distances-based or model-based), but custom\nmethods can also be easily used. III) Partition pooling. The\nset of partitions is aggregated using Non-negative Matrix\nFactorization based method. An associated instability measure\nis computed by bootstrap (see Fang, Y. and Wang, J., 2012\n). Among applications, this\ninstability measure can be used to choose a number of clusters\nwith missing values. The package also proposes several\ndiagnostic tools to tune the number of imputed data sets, to\ntune the number of iterations in fully sequential imputation,\nto check the fit of imputation models, etc.", "License": "GPL-2 | GPL-3", "RoxygenNote": "7.3.3", "VignetteBuilder": "R.rsp", "LazyData": "true", "RcppModules": "IO_module", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-09-28 19:37:14 UTC", "User": "root" }, "Author": "Vincent Audigier [aut, cre] (CNAM MSDMA team), Hang Joon Kim\n[ctb] (University of Cincinnati)", "Maintainer": "Vincent Audigier ", "Config/pak/sysreqs": "cmake libfreetype6-dev libgmp3-dev make libgsl0-dev\nlibicu-dev libpng-dev libuv1-dev libmpfr-dev libx11-dev\nzlib1g-dev", "Repository": "https://vaudigier.r-universe.dev", "Date/Publication": "2026-04-03 15:05:36 UTC", "RemoteUrl": "https://github.com/cran/clusterMI", "RemoteRef": "HEAD", "RemoteSha": "5319494a755b56b3833ebeba6a7baaafb24794bb", "_user": "vaudigier", "_type": "src", "_file": 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