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type: software
license: MIT
title: 'pams: Profile Analysis via Multidimensional Scaling'
version: 0.1.0
doi: 10.32614/CRAN.package.pams
abstract: Implements Profile Analysis via Multidimensional Scaling (PAMS) for the
  identification of population-level core response profiles from cross-sectional and
  longitudinal person-score data. Each person profile is decomposed into a level component
  (the person mean) and a pattern component (ipsatized subscores). PAMS uses nonmetric
  multidimensional scaling via the SMACOF algorithm (de Leeuw & Mair, 2009) to identify
  a small number of core profiles that represent the central response patterns in
  a sample of any size. Bootstrap standard errors and bias-corrected and accelerated
  (BCa) confidence intervals for individual core profile coordinates are estimated,
  enabling significance testing of coordinates that is not available in other profile
  analysis methods such as cluster profile analysis or latent profile analysis. Person-level
  weights, R-squared values, and correlations with core profiles are also estimated,
  allowing individual profiles to be interpreted in terms of the core profile structure.
  PAMS can be applied to both cross-sectional data and longitudinal data, where core
  trajectory profiles describe how response patterns change over time.
authors:
- family-names: Kim
  given-names: Se-Kang
  email: Se-Kang.Kim@bcm.edu
  orcid: https://orcid.org/0000-0002-2460-3415
- family-names: Kim
  given-names: Donghoh
  email: donghoh.kim@gmail.com
  orcid: https://orcid.org/0000-0002-3946-0777
repository: https://sekangakim.r-universe.dev
repository-code: https://github.com/sekangakim/pams
commit: e82f5e86dff21630238e7b68c78b5a29466047a2
url: https://github.com/sekangakim/pams
date-released: '2026-03-22'
contact:
- family-names: Kim
  given-names: Se-Kang
  email: Se-Kang.Kim@bcm.edu
  orcid: https://orcid.org/0000-0002-2460-3415