stops: Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodnes-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissmiliarities, SMACOF MDS with powers of dissmiliarities, Sammon mapping with powers of dissmiliarities, elastic scaling with powers of dissmiliarities, spherical SMACOF with powers of dissmiliarities, (ALSCAL) s-stress MDS with powers of dissmiliarities, r-stress MDS, MDS with powers of dissmiliarities and configuration distances, elastic scaling powers of dissmiliarities and configuration distances, Sammon mapping powers of dissmiliarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on godness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

install.packages("stops", repos="http://R-Forge.R-project.org")

Try the stops package in your browser