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.
| Version: | 1.12-1 |
| Depends: | R (≥ 3.5.0),cordillera (≥ 0.7-2),smacofx |
| Imports: | smacof,analogue,cmaes,crs,dfoptim,GenSA,minqa,NlcOptim,nloptr,pso,rgenoud,Rsolnp,subplex |
| Suggests: | R.rsp,rmarkdown |
| Published: | 2024-09-22 |
| DOI: | 10.32614/CRAN.package.cops |
| Author: | Thomas Rusch [aut, cre], Patrick Mair [aut], Kurt Hornik [ctb] |
| 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 |
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