smacofx

Flexible multidimensional scaling (MDS) methods and extensions to the package 'smacof'. This package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different flexible MDS models (some as of yet unpublished). These are: Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969, doi:10.1109/T-C.1969.222678) with ratio and interval optimal scaling, Multiscale MDS (Ramsay, 1977, doi:10.1007/BF02294052) with ratio and interval optimal scaling, S-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977, doi:10.1007/BF02293745) with ratio and interval optimal scaling, elastic scaling (McGee, 1966, doi:10.1111/j.2044-8317.1966.tb00367.x) with ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair, 2016, https://rpubs.com/deleeuw/142619) with ratio, interval and non-metric optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002 doi:10.1007/s00357-001-0031-0) with ratio and interval optimal scaling, restricted power-stress (Rusch, Mair & Hornik, 2021, doi:10.1080/10618600.2020.1869027) with ratio and interval optimal scaling, approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021, doi:10.1080/10618600.2020.1869027), Box-Cox MDS (Chen & Buja, 2013, https://jmlr.org/papers/v14/chen13a.html), local MDS (Chen & Buja, 2009, doi:10.1198/jasa.2009.0111), curvilinear component analysis (Demartines & Herault, 1997, doi:10.1109/72.554199) and curvilinear distance analysis (Lee, Lendasse & Verleysen, 2004, doi:10.1016/j.neucom.2004.01.007). There also are experimental models (e.g., sparsified MDS and sparsified POST-MDS). Some functions are suitably flexible to allow any other sensible combination of explicit power transformations for weights, distances and input proximities with implicit ratio, interval or non-metric optimal scaling of the input proximities. Most functions use a Majorization-Minimization algorithm. Currently the methods are only available for one-mode data (symmetric dissimilarity matrices).

Last 30 days

This package has been downloaded 264 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 11 times.

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Last 365 days

This package has been downloaded 3,806 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 19, 2024 with 70 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.