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The SCCA package implements in R the methodological approach to CA as proposed inCorrespondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexityvan Dam et al; 2021.

The package can be installed directly from Github with the code below. Ensure the packagedevtools has been installed.

#install.packages("devtools")library(devtools)install_github("UtrechtUniversity/scca", build_vignettes = TRUE)

After loading the package a list of all exported functions and data sets can be retrieved by?SCCA and the documentation of an individual function by?<function name>; e.g.?scca_compute.

The methodology and the use of the functions and the data are explained in the included vignette. After installing package SCCA usebrowseVignettes('SCCA') in the R(Studio) console.

The software code is licensed underMIT. The next section (References) provides links tosources of the included datasets. See there for licences of those data sets.

van Dam, Alje, Dekker, Mark, Morales-Castilla, Ignacio, Rodríguez, Miguel Á., Wichmann, David and Baudena, Mara (2021); Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity;Scientific Reports; DOI: 10.1038/s41598-021-87971-9

Faurby, Søren e.a; 2019;HYLACINE 1.2: The Phylogenetic Atlas of Mammal Macroecology

The team members are:

• Mathematical foundations of the code

  • Alje van Dam, Copernicus Institute of Sustainable Development and Centre for Complex Systems Studies, Utrecht University, the Netherlands
  • Mark Dekker, Department of Information and Computing Sciences and Centre for Complex Systems Studies, Utrecht University, the Netherlands

• Programming and packaging

  • Kees van Eijden Research Engineering/ITS, Utrecht University, the Netherlands

• With contributions of

  • Ignacio Morales Castilla, Global Change Ecology and Evolution Group, Department of Life Sciences, University of Alcala´, Spain
  • Jonathan de Bruin, Research Engineering/ITS, Utrecht University, the Netherlands
  • Raoul Schram, Research Engineering/ITS, Utrecht University, the Netherlands
  • Mara Baudena, National Research Council of Italy, Institute of Atmospheric Science and Climate (CNR-ISAC), Turin, Italy; Copernicus Institute of Sustainable Development and Centre for Complex Systems Studies, Utrecht University, the Netherlands

To cite the SCCA repository and R package, usecitation("SCCA") to retrieve the BibTex entry. Otherwise use the following format:

van Eijden, Kees et al; 2021; SCCA: Spectral Clustering Correspondence Analysis in R; Utrecht University; DOI: 10.5281/zenodo.4665670.Also available atUtrecht University.

Please also cite the papervan Dam et al, 2021 when using the SCCA repository.