Sparse generalized dissimilarity modeling (SGDM) is a two-stage method consisting of initially reducing the environmental data by means of a sparse canonical correlation analysis, to then fit the resulting sparse canonical components in a generalized dissimilarity model (GDM). The package also includes additional tools, useful for both GDM and SGDM.
|Author||Pedro J. Leitão, Marcel Schwieder, Cornelius Senf|
|Maintainer||Pedro J. Leitão <[email protected]>, Cornelius Senf <[email protected]>|
|Package repository||View on GitHub|
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