{ "_id": "6a1bff781d7bb097a0a28fc6", "Package": "pams", "Type": "Package", "Title": "Profile Analysis via Multidimensional Scaling", "Version": "0.1.0", "Date": "2026-03-22", "Authors@R": "c(\nperson(\"Se-Kang\", \"Kim\",\nemail = \"Se-Kang.Kim@bcm.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-2460-3415\")),\nperson(\"Donghoh\", \"Kim\",\nemail = \"donghoh.kim@gmail.com\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-3946-0777\")))", "Description": "Implements Profile Analysis via Multidimensional Scaling\n(PAMS) for the identification of population-level core response\nprofiles from cross-sectional and longitudinal person-score\ndata. Each person profile is decomposed into a level component\n(the person mean) and a pattern component (ipsatized\nsubscores). PAMS uses nonmetric multidimensional scaling via\nthe SMACOF algorithm (de Leeuw & Mair, 2009) to identify a\nsmall number of core profiles that represent the central\nresponse patterns in a sample of any size. Bootstrap standard\nerrors and bias-corrected and accelerated (BCa) confidence\nintervals for individual core profile coordinates are\nestimated, enabling significance testing of coordinates that is\nnot available in other profile analysis methods such as cluster\nprofile analysis or latent profile analysis. Person-level\nweights, R-squared values, and correlations with core profiles\nare also estimated, allowing individual profiles to be\ninterpreted in terms of the core profile structure. PAMS can be\napplied to both cross-sectional data and longitudinal data,\nwhere core trajectory profiles describe how response patterns\nchange over time.", "License": "MIT + file LICENSE", "URL": "https://github.com/sekangakim/pams", "BugReports": "https://github.com/sekangakim/pams/issues", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libx11-dev\nzlib1g-dev", "Repository": "https://sekangakim.r-universe.dev", "Date/Publication": "2026-03-27 03:47:08 UTC", "RemoteUrl": "https://github.com/sekangakim/pams", "RemoteRef": "HEAD", "RemoteSha": "e82f5e86dff21630238e7b68c78b5a29466047a2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-31 09:24:53 UTC", "User": "root" }, "Author": "Se-Kang Kim [aut, cre] (ORCID: ),\nDonghoh Kim [aut] (ORCID: )", "Maintainer": "Se-Kang Kim ", "MD5sum": "4679280980e6aac0d5b79e928037b70c", "_user": "sekangakim", "_type": "src", "_file": "pams_0.1.0.tar.gz", 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], "_help": [ { "page": "BootSmacof", "title": "Profile Analysis via Multidimensional Scaling", "topics": [ "BootSmacof" ] } ], "_readme": "https://github.com/sekangakim/pams/raw/HEAD/README.md", "_rundeps": [ "backports", "base64enc", "bit", "bit64", "boot", "broom", "bslib", "cachem", "checkmate", "class", "cli", "clipr", "cluster", "codetools", "colorspace", "cpp11", "crayon", "data.table", "digest", "doParallel", "dplyr", "e1071", "ellipse", "evaluate", "farver", "fastmap", "fontawesome", "forcats", "foreach", "foreign", "Formula", "fs", "gdata", "generics", "ggplot2", "glmnet", "glue", "gridExtra", "gtable", "gtools", "haven", "highr", "Hmisc", "hms", "htmlTable", "htmltools", "htmlwidgets", "isoband", "iterators", "jomo", "jquerylib", "jsonlite", "knitr", "labeling", "lattice", "lifecycle", "lme4", "magrittr", "MASS", "Matrix", "memoise", "mice", "mime", "minqa", "mitml", "nlme", "nloptr", "nnet", "nnls", "numDeriv", "ordinal", "pan", "pillar", "pkgconfig", "plotrix", "polynom", 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