{ "_id": "6a1effb5b401979e7341aaac", "Package": "stops", "Title": "Structure Optimized Proximity Scaling", "Version": "1.10-1", "Authors@R": "c(person(given=\"Thomas\", family=\"Rusch\", email=\"thomas.rusch@wu.ac.at\", role = c(\"aut\",\"cre\"),comment = c(ORCID = \"0000-0002-7773-2096\")), person(given=\"Patrick\", family=\"Mair\", role = \"aut\",comment=c(ORCID=\"0000-0003-0100-6511\")), person(given=\"Kurt\",family=\"Hornik\", role = \"ctb\", email = \"Kurt.Hornik@R-project.org\", comment = c(ORCID = \"0000-0003-4198-9911\")))", "Maintainer": "Thomas Rusch ", "Description": "Methods that use flexible variants of multidimensional\nscaling (MDS) which incorporate parametric nonlinear distance\ntransformations and trade-off the goodness-of-fit fit with\nstructure considerations to find optimal hyperparameters, also\nknown as structure optimized proximity scaling (STOPS) (Rusch,\nMair & Hornik, 2023,). The\npackage contains various functions, wrappers, methods and\nclasses for fitting, plotting and displaying different 1-way\nMDS models with ratio, interval, ordinal optimal scaling in a\nSTOPS framework. These cover essentially the functionality of\nthe package smacofx, including Torgerson (classical) scaling\nwith power transformations of dissimilarities, SMACOF MDS with\npowers of dissimilarities, Sammon mapping with powers of\ndissimilarities, elastic scaling with powers of\ndissimilarities, spherical SMACOF with powers of\ndissimilarities, (ALSCAL) s-stress MDS with powers of\ndissimilarities, r-stress MDS, MDS with powers of\ndissimilarities and configuration distances, elastic scaling\npowers of dissimilarities and configuration distances, Sammon\nmapping powers of dissimilarities and configuration distances,\npower stress MDS (POST-MDS), approximate power stress, Box-Cox\nMDS, local MDS, Isomap, curvilinear component analysis (CLCA),\ncurvilinear distance analysis (CLDA) and sparsified (power)\nmultidimensional scaling and (power) multidimensional distance\nanalysis (experimental models from smacofx influenced by CLCA).\nAll of these models can also be fit by optimizing over\nhyperparameters based on goodness-of-fit fit only (i.e., no\nstructure considerations). The package further contains\nfunctions for optimization, specifically the adaptive\nLuus-Jaakola algorithm and a wrapper for Bayesian optimization\nwith treed Gaussian process with jumps to linear models, and\nfunctions for various c-structuredness indices. Hyperparameter\noptimization can be done with a number of techniques but we\nrecommend either Bayesian optimization or particle swarm. For\nusing \"Kriging\", users need to install a version of the\narchived 'DiceOptim' R package.", "License": "GPL-2 | GPL-3", "LazyData": "true", "URL": "https://r-forge.r-project.org/projects/stops/", "VignetteBuilder": "R.rsp", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "cmake make libgsl0-dev default-jdk libicu-dev\nlibuv1-dev libssl-dev libx11-dev zlib1g-dev", "Repository": "https://r-forge.r-universe.dev", "Date/Publication": "2026-01-18 13:38:14 UTC", "RemoteUrl": "https://github.com/r-forge/stops", "RemoteRef": "HEAD", "RemoteSha": "a51c031eed4408589a889df5bd68990edba5c9a8", "RemoteSubdir": "pkg/stops", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-18 09:13:42 UTC", "User": "root" }, "Author": "Thomas Rusch [aut, cre] (ORCID:\n),\nPatrick Mair [aut] (ORCID: ),\nKurt Hornik [ctb] (ORCID: )", "MD5sum": "3211805a099470986f3c548fe52b7e88", "_user": "r-forge", "_type": "src", "_file": "stops_1.10-1.tar.gz", "_fileid": 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"OK", "artifact": "7363246846" }, { "job": 79116184158, "time": 185, "config": "windows-devel", "r": "4.7.0", "check": "WARNING", "artifact": "7053919113" }, { "job": 79116184437, "time": 177, "config": "windows-oldrel", "r": "4.5.3", "check": "WARNING", "artifact": "7053915831" }, { "job": 79116184066, "time": 196, "config": "windows-release", "r": "4.6.0", "check": "WARNING", "artifact": "7053922416" } ], "_buildurl": "https://github.com/r-universe/r-forge/actions/runs/26024157981", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/r-forge/stops", "_commit": { "id": "a51c031eed4408589a889df5bd68990edba5c9a8", "author": "trusch (Thomas Rusch) ", "committer": "trusch (Thomas Rusch) ", "message": "update HP\n\ngit-svn-id: svn://svn.r-forge.r-project.org/svnroot/stops@539 edb9625f-4e0d-4859-8d74-9fd3b1da38cb\n", "time": 1768743494 }, "_maintainer": { "name": "Thomas Rusch", "email": "thomas.rusch@wu.ac.at", "orcid": "0000-0002-7773-2096" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "smacofx", "role": "Depends" }, { "package": "acepack", "role": "Imports" }, { "package": "clue", "role": "Imports" }, { "package": "cmaes", "role": "Imports" }, { "package": "cordillera", "role": "Imports" }, { "package": "dfoptim", "role": "Imports" }, { "package": "energy", "role": "Imports" }, { "package": "minerva", "role": "Imports" }, { "package": "nloptr", "role": "Imports" }, { "package": "pomp", "role": "Imports" }, { "package": "pso", "role": "Imports" }, { "package": "registry", "role": "Imports" }, { "package": "scagnostics", "role": "Imports" }, { "package": "smacof", "role": "Imports" }, { "package": "tgp", "role": "Imports" }, { "package": "vegan", "role": "Imports" }, { "package": "R.rsp", "role": "Suggests" }, { "package": "DiceOptim", "role": "Suggests" }, { "package": "DiceKriging", "role": "Suggests" }, { "package": "stats", "role": "Enhances" } ], "_owner": 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"version": "1.0-1", "date": "2023-01-20" }, { "version": "1.6-2", "date": "2024-06-28" }, { "version": "1.8-2", "date": "2024-09-22" }, { "version": "1.9-1", "date": "2025-04-28" } ], "_exports": [ "c_association", "c_clumpiness", "c_clusteredness", "c_complexity", "c_convexity", "c_dependence", "c_faithfulness", "c_functionality", "c_hierarchy", "c_inequality", "c_linearity", "c_manifoldness", "c_nonmonotonicity", "c_outlying", "c_regularity", "c_shepardness", "c_skinniness", "c_sparsity", "c_striatedness", "c_stringiness", "ljoptim", "stop_bcmds", "stop_clca", "stop_cldae", "stop_cldak", "stop_elastic", "stop_isomap1", "stop_isomap2", "stop_lmds", "stop_powerelastic", "stop_powermds", "stop_powersammon", "stop_powerstress", "stop_rstress", "stop_sammon", "stop_sammon2", "stop_smacofSphere", "stop_smacofSym", "stop_smddae", "stop_smddak", "stop_smds", "stop_spmddae", "stop_spmddak", "stop_spmds", "stop_sstress", "stoploss", "stops", "tgpoptim" ], "_datasets": [ { "name": "BankingCrisesDistances", "title": "Banking Crises Distances", "object": "BankingCrisesDistances", "class": [ "matrix", "array" ], "fields": [ "Algeria", "Angola", "Argentina", "Australia", "Austria", "Belgium", "Bolivia", "Brazil", "Canada", "CentralAfricanRep", "Chile", "China", "Colombia", "CostaRica", "CoteDIvoire", "Denmark", "DominicanRepublic", "Ecuador", "Egypt", "ElSalvador", "Finland", "France", "Germany", "Ghana", "Guatemala", "Honduras", "Hun/Gre", "Iceland", "India", "Indonesia", "Ireland", "Italy", "Japan", "Kenya", "Korea", "Malaysia", "Mauritius", "Mexico", "Morocco", "Myanmar", "Netherlands", "NewZealand", "Nicaragua", "Nigeria", "Norway", "Panama", "Paraguay", "Peru", "Philippines", "Poland", "Portugal", "Romania", "Russia", "Singapore", "SouthAfrica", "Spain", "SriLanka", "Sweden", "Switzerland", "Taiwan", "Thailand", "Tunisia", "Turkey", "UK", "Uruguay", "US", "Venezuela", "Zambia", "Zimbabwe", "richpoor" ], "rows": 69, "table": true, "tojson": true }, { "name": "Pendigits500", "title": "Pen digits", "object": "Pendigits500", "class": [ "data.frame" ], "fields": [ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "digit" ], "rows": 500, "table": true, "tojson": true }, { "name": "Swissroll", "title": "Swiss roll", "object": "Swissroll", "class": [ "data.frame" ], "fields": [ "x", "y", "z", "col" ], "rows": 150, "table": true, "tojson": true } ], "_help": [ { "page": "BankingCrisesDistances", "title": "Banking Crises Distances", "topics": [ "BankingCrisesDistances" ] }, { "page": "c_association", "title": "c-association calculates the c-association based on the maximal information coefficient We define c-association as the aggregated association between any two columns in confs", "topics": [ "c_association" ] }, { "page": "c_clumpiness", "title": "c-clumpiness", "topics": [ "c_clumpiness" ] }, { "page": "c_clusteredness", "title": "c-clusteredness calculates c-clusteredness as the OPTICS cordillera. The higher the more clustered.", "topics": [ "c_clusteredness" ] }, { "page": "c_complexity", "title": "c-complexity Calculates the c-complexity based on the minimum cell number We define c-complexity as the aggregated minimum cell number between any two columns in confs This is one of few c-structuredness indices not between 0 and 1, but can be between 0 and (theoretically) infinity", "topics": [ "c_complexity" ] }, { "page": "c_convexity", "title": "c-convexity", "topics": [ "c_convexity" ] }, { "page": "c_dependence", "title": "c-dependence calculates c-dependence as the aggregated distance correlation of each pair if nonidentical columns", "topics": [ "c_dependence" ] }, { "page": "c_faithfulness", "title": "c-faithfulness calculates the c-faithfulness based on the index by Chen and Buja 2013 (M_adj) with equal input neigbourhoods", "topics": [ "c_faithfulness" ] }, { "page": "c_functionality", "title": "c-functionality calculates the c-functionality based on the maximum edge value We define c-functionality as the aggregated functionality between any two columns of confs", "topics": [ "c_functionality" ] }, { "page": "c_hierarchy", "title": "c-hierarchy captures how well a partition/ultrametric (obtained by hclust) explains the configuration distances. Uses variance explained for euclidean distances and deviance explained for everything else.", "topics": [ "c_hierarchy" ] }, { "page": "c_inequality", "title": "c-inequality Calculates c-inequality (as in an economic measure of inequality) as Pearsons coefficient of variation of the fitted distance matrix. This can help with avoiding degenerate solutions. This is one of few c-structuredness indices not between 0 and 1, but 0 and infinity.", "topics": [ "c_inequality" ] }, { "page": "c_linearity", "title": "c-linearity calculates c-linearity as the aggregated multiple correlation of all columns of the configuration.", "topics": [ "c_linearity" ] }, { "page": "c_manifoldness", "title": "c-manifoldness calculates c-manifoldness as the aggregated maximal correlation coefficient (i.e., Pearson correlation of the ACE transformed variables) of all pairwise combinations of two different columns in confs. If there is an NA (happens usually when the optimal transformation of any variable is a constant and therefore the covariance is 0 but also one of the sds in the denominator), it gets skipped.", "topics": [ "c_manifoldness" ] }, { "page": "c_mine", "title": "wrapper for getting the mine coefficients", "topics": [ "c_mine" ] }, { "page": "c_nonmonotonicity", "title": "c-nonmonotonicity calculates the c-nonmonotonicity based on the maximum asymmetric score We define c-nonmonotonicity as the aggregated nonmonotonicity between any two columns in confs this is one of few c-structuredness indices not between 0 and 1", "topics": [ "c_nonmonotonicity" ] }, { "page": "c_outlying", "title": "c-outlying", "topics": [ "c_outlying" ] }, { "page": "c_regularity", "title": "c-regularity calculates c-regularity as 1 - OPTICS cordillera for k=2. The higher the more regular.", "topics": [ "c_regularity" ] }, { "page": "c_skinniness", "title": "c-skinniness", "topics": [ "c_skinniness" ] }, { "page": "c_sparsity", "title": "c-sparsity", "topics": [ "c_sparsity" ] }, { "page": "c_striatedness", "title": "c-striatedness", "topics": [ "c_striatedness" ] }, { "page": "c_stringiness", "title": "c-stringiness", "topics": [ "c_stringiness" ] }, { "page": "knn_dist", "title": "calculate k nearest neighbours from a distance matrix", "topics": [ "knn_dist" ] }, { "page": "ljoptim", "title": "(Adaptive) Version of Luus-Jaakola Optimization", "topics": [ "ljoptim" ] }, { "page": "Pendigits500", "title": "Pen digits", "topics": [ "Pendigits500" ] }, { "page": "plot.stops", "title": "S3 plot method for stops objects", "topics": [ "plot.stops" ] }, { "page": "stop_apstress", "title": "STOPS version of approximated power stress models.", "topics": [ "stop_apstress" ] }, { "page": "stop_cmdscale", "title": "STOPS version of strain", "topics": [ "stop_cmdscale" ] }, { "page": "stop_elastic", "title": "STOPS versions of elastic scaling models (via smacofSym)", "topics": [ "stop_elastic" ] }, { "page": "stop_isomap1", "title": "STOPS version of isomap to optimize over integer k.", "topics": [ "stop_isomap1" ] }, { "page": "stop_isomap2", "title": "STOPS version of isomap over real epsilon.", "topics": [ "stop_isomap2" ] }, { "page": "stop_lmds", "title": "STOPS version of lMDS", "topics": [ "stop_lmds" ] }, { "page": "stop_powerelastic", "title": "STOPS version of elastic scaling with powers for proximities and distances", "topics": [ "stop_powerelastic" ] }, { "page": "stop_powermds", "title": "STOPS version of powermds", "topics": [ "stop_powermds" ] }, { "page": "stop_powersammon", "title": "STOPS version of sammon with powers", "topics": [ "stop_powersammon" ] }, { "page": "stop_powerstress", "title": "STOPS version of powerstress", "topics": [ "stop_powerstress" ] }, { "page": "stop_rpowerstress", "title": "STOPS version of restricted powerstress", "topics": [ "stop_rpowerstress" ] }, { "page": "stop_rstress", "title": "STOPS version of rstress", "topics": [ "stop_rstress" ] }, { "page": "stop_sammon", "title": "STOPS version of Sammon mapping", "topics": [ "stop_sammon" ] }, { "page": "stop_sammon2", "title": "Another STOPS version of Sammon mapping models (via smacofSym)", "topics": [ "stop_sammon2" ] }, { "page": "stop_smacofSphere", "title": "STOPS versions of smacofSphere models", "topics": [ "stop_smacofSphere" ] }, { "page": "stop_smacofSym", "title": "STOPS version of smacofSym models", "topics": [ "stop_smacofSym" ] }, { "page": "stop_sstress", "title": "STOPS version of sstress", "topics": [ "stop_sstress" ] }, { "page": "stoploss", "title": "Calculate the weighted multiobjective loss function used in STOPS", "topics": [ "stoploss" ] }, { "page": "stops", "title": "High Level STOPS Function", "topics": [ "stops" ] }, { "page": "Swissroll", "title": "Swiss roll", "topics": [ "Swissroll" ] }, { "page": "tgpoptim", "title": "Bayesian Optimization by a (treed) Bayesian Gaussian Process Prior (with jumps to linear models) surrogate model Essentially a wrapper for the functionality in tgp that has the same slots as optim with defaults for STOPS models.", "topics": [ "tgpoptim" ] } ], "_rundeps": [ "abind", "acepack", "askpass", "backports", "base64enc", "bit", "bit64", "boot", "broom", "bslib", "cachem", "checkmate", "class", "cli", "clipr", "clue", "cluster", "cmaes", "coda", "codetools", "colorspace", "commonmark", "cordillera", "cpp11", "crayon", "crosstalk", "curl", "data.table", "dbscan", "deldir", "deSolve", "dfoptim", "digest", "doParallel", "dplyr", "e1071", "ellipse", "energy", "evaluate", "farver", "fastmap", "fontawesome", "forcats", "foreach", "foreign", "Formula", "fs", "gdata", "GeneralizedUmatrix", "generics", "geometry", "ggplot2", "glmnet", "glue", "gridExtra", "gsl", "gtable", "gtools", "haven", "highr", "Hmisc", "hms", "htmlTable", "htmltools", "htmlwidgets", 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