Cluster optimized proximity scaling (COPS) refers to 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, approximated power stress, power elastic scaling, power Sammon mapping (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-Jakola Algorithm'' (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027> ).
MASS, minqa, pso, scatterplot3d, NlcOptim, Rsolnp, dfoptim,
subplex, cmaes, crs, nloptr, rgenoud, GenSA

Classes (S4) of commonly used copulas including
elliptical, and Archimidean copulas. Implemented copulas
include normal, t, Clayton, Frank, and Gumbel.
Methods for density, distribution, random number generators,
persp, and contour.