{ "_id": "6ace0777a14063b82c3aeae8", "Package": "fmds", "Type": "Package", "Title": "Multidimensional Scaling Development Kit", "Version": "0.1.5", "Authors@R": "c(person(given = \"Frank M.T.A.\", family = \"Busing\", \nrole = c(\"aut\", \"cre\"),\nemail = \"busing@fsw.leidenuniv.nl\",\ncomment = c( ORCID = \"0000-0002-8062-538X\")),\nperson(\"Juan\", \"Claramunt Gonzalez\",\nrole = c(\"aut\", \"com\"),\nemail = \"j.claramunt.gonzalez@fsw.leidenuniv.nl\",\ncomment = c( ORCID = \"0009-0009-5387-6341\")))", "Author": "Frank M.T.A. Busing [aut, cre] (ORCID:\n), Juan Claramunt\nGonzalez [aut, com] (ORCID:\n)", "Maintainer": "Frank M.T.A. Busing ", "Description": "Multidimensional scaling (MDS) functions for various tasks\nthat are beyond the beta stage and way past the alpha stage.\nCurrently, options are available for weights, restrictions,\nclassical scaling or principal coordinate analysis,\ntransformations (linear, power, Box-Cox, spline, ordinal),\noutlier mitigation (rdop), out-of-sample estimation (predict),\nnegative dissimilarities, fast and faster executions with low\nmemory footprints, penalized restrictions,\ncross-validation-based penalty selection, supplementary\nvariable estimation (explain), additive constant estimation,\nmixed measurement level distance calculation, restricted\nclassical scaling, etc. More will come in the future.\nReferences. Busing (2024) \"A Simple Population Size Estimator\nfor Local Minima Applied to Multidimensional Scaling\".\nManuscript submitted for publication. Busing (2025) \"Node\nLocalization by Multidimensional Scaling with Iterative\nMajorization\". Manuscript submitted for publication. Busing\n(2025) \"Faster Multidimensional Scaling\". Manuscript in\npreparation. Barroso and Busing (2025) \"e-RDOP, Relative\nDensity-Based Outlier Probabilities, Extended to Proximity\nMapping\". 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