English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The generalized singular value decomposition (GSVD, a.k.a. "SVD triplet","duality diagram" approach) provides a unified strategy and basis to performnearly all of the most common multivariate analyses (e.g., principalcomponents, correspondence analysis, multidimensional scaling, canonicalcorrelation, partial least squares). Though the GSVD is ubiquitous, powerful,and flexible, it has very few implementations. Here I introduce the GSVDpackage for R. The general goal of GSVD is to provide a small set of accessiblefunctions to perform the GSVD and two other related decompositions (generalizedeigenvalue decomposition, generalized partial least squares-singular valuedecomposition). Furthermore, GSVD helps provide a more unified conceptualapproach and nomenclature to many techniques. I first introduce the concept ofthe GSVD, followed by a formal definition of the generalized decompositions.Next I provide some key decisions made during development, and then a number ofexamples of how to use GSVD to implement various statistical techniques. Theseexamples also illustrate one of the goals of GSVD: how others can (or should)build analysis packages that depend on GSVD. Finally, I discuss the possiblefuture of GSVD.

Generalized eigen, singular value, and partial least squares decompositions: The GSVD package