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  "Title": "Profile Analysis via Multidimensional Scaling",
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  "Date": "2026-10-05",
  "Authors@R": "c(\nperson(\"Se-Kang\", \"Kim\",\nemail = \"sekangandroid@gmail.com\",\nrole  = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0928-3396\")),\nperson(\"Donghoh\", \"Kim\",\nemail = \"donghoh.kim@gmail.com\",\nrole  = \"aut\",\ncomment = c(ORCID = \"0000-0001-7031-5639\")))",
  "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 to identify a small number of core\nprofiles that represent the central response patterns in a\nsample of any size. Bootstrap standard errors and\nbias-corrected and accelerated (BCa) confidence intervals for\nindividual core profile coordinates are estimated, enabling\nsignificance testing of coordinates that is not available in\nother profile analysis methods such as cluster profile analysis\nor latent profile analysis. Person-level weights, R-squared\nvalues, and partial correlations with core profiles are also\nestimated, allowing individual profiles to be interpreted in\nterms of the core profile structure. PAMS can be applied to\nboth cross-sectional data and longitudinal data, where core\ntrajectory profiles describe how response patterns change over\ntime. Methods are described in Kim and Kim (2024)\n<doi:10.20982/tqmp.20.3.p230>, de Leeuw and Mair (2009)\n<doi:10.18637/jss.v031.i03>, and Kruskal (1964)\n<doi:10.1007/BF02289565>.",
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