Other packages > Find by keyword >

cops  

Cluster Optimized Proximity Scaling
View on CRAN: Click here


Download and install cops package within the R console
Install from CRAN:
install.packages("cops")

Install from Github:
library("remotes")
install_github("cran/cops")

Install by package version:
library("remotes")
install_version("cops", "1.12-1")



Attach the package and use:
library("cops")
Maintained by
Thomas Rusch
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-11-01
Latest Update: 2024-09-22
Description:
Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, ). 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, ). 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, ), Sammon mapping (Sammon, 1969, ), elastic scaling (McGee, 1966, ), s-stress (Takane, Young & de Leeuw, 1977, ), r-stress (de Leeuw, Groenen & Mair, 2016, ), power stress (Buja & Swayne, 2002 ), restricted power stress, approximate power stress, power elastic scaling, power Sammon mapping (for all Rusch, Mair & Hornik, 2021, ). 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,).
How to cite:
Thomas Rusch (2019). cops: Cluster Optimized Proximity Scaling. R package version 1.12-1, https://cran.r-project.org/web/packages/cops. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:28), 1.0-2 (2019-11-01 10:30), 1.2-0 (2021-03-23 14:50), 1.3-1 (2023-01-19 16:50), 1.11-3 (2024-06-27 13:10)
Other packages that cited cops R package
View cops citation profile
Other R packages that cops depends, imports, suggests or enhances
Complete documentation for cops
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

eyelinker  
Import ASC Files from EyeLink Eye Trackers
Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for ...
Download / Learn more Package Citations See dependency  
downlit  
Syntax Highlighting and Automatic Linking
Syntax highlighting of R code, specifically designed for the needs of 'RMarkdown' packages like 'pk ...
Download / Learn more Package Citations See dependency  
injectoR  
R Dependency Injection
R dependency injection framework. Dependency injection allows a program design to follow the depend ...
Download / Learn more Package Citations See dependency  
hmeasure  
The H-Measure and Other Scalar Classification Performance Metrics
Classification performance metrics that are derived from the ROC curve of a classifier. The package ...
Download / Learn more Package Citations See dependency  
data360r  
Wrapper for 'TCdata360' and 'Govdata360' API
Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdat ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  

28,565

R Packages

239,283

Dependencies

75,677

Author Associations

28,566

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA