R package smacof: SMACOF for Multidimensional Scaling. This package provides the following approaches of multidimensional scaling (MDS) based on stress minimization by means of majorization (smacof): Simple smacof on symmetric dissimilarity matrices, smacof for rectangular matrices (unfolding models), smacof with constraints on the configuration, three-way smacof for individual differences (including constraints for idioscal, indscal, and identity), and spherical smacof (primal and dual algorithm). Each of these approaches is implemented in a metric and nonmetric manner including primary, secondary, and tertiary approaches for tie handling. Jackknife and permutation tests are included as well.

References in zbMATH (referenced in 30 articles , 1 standard article )

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  1. Mair, P., Groenen, P. J. F., de Leeuw, J.: More on Multidimensional Scaling and Unfolding in R: smacof Version 2 (2022) not zbMATH
  2. Arias-Castro, Ery; Channarond, Antoine; Pelletier, Bruno; Verzelen, Nicolas: On the estimation of latent distances using graph distances (2021)
  3. Chen, Yunxiao; Ying, Zhiliang; Zhang, Haoran: Unfolding-model-based visualization: theory, method and applications (2021)
  4. Dzemyda, Gintautas; Sabaliauskas, Martynas: Geometric multidimensional scaling: a new approach for data dimensionality reduction (2021)
  5. Sergio Venturini, Raffaella Piccarreta : A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R (2021) not zbMATH
  6. Dzemyda, Gintautas; Sabaliauskas, Martynas: A novel geometric approach to the problem of multidimensional scaling (2020)
  7. Graffelman, Jan: Goodness-of-fit filtering in classical metric multidimensional scaling with large datasets (2020)
  8. Lu, Si-Tong; Zhang, Miao; Li, Qing-Na: Feasibility and a fast algorithm for Euclidean distance matrix optimization with ordinal constraints (2020)
  9. Zhou, Shenglong; Xiu, Naihua; Qi, Hou-Duo: Robust Euclidean embedding via EDM optimization (2020)
  10. Hennig, Christian; Sauerbrei, Willi: Exploration of the variability of variable selection based on distances between bootstrap sample results (2019)
  11. Borg, Ingwer; Groenen, Patrick J. F.; Mair, Patrick: Applied multidimensional scaling and unfolding (2018)
  12. Bove, Giuseppe; Okada, Akinori: Methods for the analysis of asymmetric pairwise relationships (2018)
  13. Mair, Patrick: Modern psychometrics with R (2018)
  14. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  15. Schäfer, Dirk; Hüllermeier, Eyke: Dyad ranking using Plackett-Luce models based on joint feature representations (2018)
  16. Greenacre, Michael J.; Groenen, Patrick J. F.: Weighted Euclidean biplots (2016)
  17. Gruenhage, Gina; Opper, Manfred; Barthelme, Simon: Visualizing the effects of a changing distance on data using continuous embeddings (2016)
  18. Yang, Bo; Xiang, Ming; Zhang, Yupei: Multi-manifold discriminant Isomap for visualization and classification (2016)
  19. Bai, Shuanghua; Qi, Huo-Duo; Xiu, Naihua: Constrained best Euclidean distance embedding on a sphere: a matrix optimization approach (2015)
  20. Mateu, Jorge; Schoenberg, Frederic P.; Diez, David M.; González, Jonatan A.; Lu, Weipeng: On measures of dissimilarity between point patterns: classification based on prototypes and multidimensional scaling (2015)

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