The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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 power functions and augment 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) for ratio, power, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj>), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>), r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>), restricted power stress, approximate power stress, power elastic scaling, power Sammon mapping (for all Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). All of these models can also solely be fit as MDS with power transformations. 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>).

Version: 1.3-1
Depends: R (≥ 3.1.2), cordillera (≥ 0.7-2), smacof (≥ 1.10-4)
Imports: MASS, minqa, pso, scatterplot3d, NlcOptim, Rsolnp, dfoptim, subplex, cmaes, crs, nloptr, rgenoud, GenSA
Suggests: R.rsp, rmarkdown
Published: 2023-01-19
Author: Thomas Rusch ORCID iD [aut, cre], Jan de Leeuw [aut], Patrick Mair [aut]
Maintainer: Thomas Rusch <thomas.rusch at wu.ac.at>
License: GPL-2 | GPL-3
URL: https://r-forge.r-project.org/projects/stops/
NeedsCompilation: no
Citation: cops citation info
Materials: NEWS
In views: Psychometrics
CRAN checks: cops results

Documentation:

Reference manual: cops.pdf
Vignettes: A Tutorial on Cluster Optimized Proximity Scaling (COPS)

Downloads:

Package source: cops_1.3-1.tar.gz
Windows binaries: r-devel: cops_1.3-1.zip, r-release: cops_1.3-1.zip, r-oldrel: cops_1.3-1.zip
macOS binaries: r-release (arm64): cops_1.3-1.tgz, r-oldrel (arm64): cops_1.3-1.tgz, r-release (x86_64): cops_1.3-1.tgz, r-oldrel (x86_64): cops_1.3-1.tgz
Old sources: cops archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=cops to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.