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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