Package: cops 1.11-4

Thomas Rusch

cops: Cluster Optimized Proximity Scaling

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

Authors:Thomas Rusch [aut, cre], Patrick Mair [aut], Kurt Hornik [ctb]

cops_1.11-4.tar.gz
cops_1.11-4.zip(r-4.5)cops_1.11-4.zip(r-4.4)cops_1.11-4.zip(r-4.3)
cops_1.11-4.tgz(r-4.4-any)cops_1.11-4.tgz(r-4.3-any)
cops_1.11-4.tar.gz(r-4.5-noble)cops_1.11-4.tar.gz(r-4.4-noble)
cops_1.11-4.tgz(r-4.4-emscripten)cops_1.11-4.tgz(r-4.3-emscripten)
cops.pdf |cops.html✨
cops/json (API)
NEWS

Peer review:

Bug tracker:https://r-forge.r-project.org/projects/stops

On CRAN:

7 exports 1 stars 1.77 score 180 dependencies 23 scripts 346 downloads

Last updated 1 months agofrom:81db9684f5. Checks:OK: 1 WARNING: 6. Indexed: yes.

Exports:copscopsccopstressMincopStressMinljoptimpcopsphidistance

Dependencies:abindanalogueaskpassbackportsbase64encbitbit64bootbrglmbroombslibcachemcandisccarcarDatacheckmateclassclicliprclustercmaescodetoolscolorspacecommonmarkcordilleracowplotcpp11crayoncrosstalkcrscubaturecurldata.tabledbscandeldirDerivdfoptimdigestdoBydoParalleldplyre1071ellipseevaluatefansifarverfastmapfontawesomeforcatsforeachforeignFormulafsgdataGeneralizedUmatrixgenericsGenSAgeometryggplot2glmnetgluegridExtragtablegtoolshavenheplotshighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjomojquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelinproglme4lpSolvemagicmagrittrMASSMatrixMatrixModelsmemoisemgcvmicemicrobenchmarkmimeminqamitmlmodelrmunsellNlcOptimnlmenloptrnnetnnlsnpnumDerivopensslordinalpanpbkrtestpermutepillarpkgconfigplotlyplotrixpolynomprettyunitsprincurveprofileModelprogressProjectionBasedClusteringpromisesproxypsopurrrquadprogquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppProgressreadrrgenoudrglrlangrmarkdownrpartRsolnprstudioapisassscalesshapeshinyshinyjsshinythemessmacofsmacofxsourcetoolsSparseMstringistringrsubplexsurvivalsystibbletidyrtidyselecttinytextruncnormtzdbucminfutf8vctrsveganviridisviridisLitevroomweightswithrwordcloudxfunxtableyaml