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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