Package: easyCODA 0.40.2

easyCODA: Compositional Data Analysis in Practice

Univariate and multivariate methods for compositional data analysis, based on logratios. The package implements the approach in the book Compositional Data Analysis in Practice by Michael Greenacre (2018), where accent is given to simple pairwise logratios. Selection can be made of logratios that account for a maximum percentage of logratio variance. Various multivariate analyses of logratios are included in the package.

Authors:Michael Greenacre [aut, cre]

easyCODA_0.40.2.tar.gz
easyCODA_0.40.2.zip(r-4.7)easyCODA_0.40.2.zip(r-4.6)easyCODA_0.40.2.zip(r-4.5)
easyCODA_0.40.2.tgz(r-4.6-any)easyCODA_0.40.2.tgz(r-4.5-any)
easyCODA_0.40.2.tar.gz(r-4.7-any)easyCODA_0.40.2.tar.gz(r-4.6-any)
easyCODA_0.40.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
easyCODA/json (API)

# Install 'easyCODA' in R:
install.packages('easyCODA', repos = c('https://michaelgreenacre.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/michaelgreenacre/codainpractice/issues

Datasets:
  • cups - Dataset: RomanCups
  • fish - Dataset: FishMorphology
  • time - Dataset: TimeBudget
  • veg - Dataset: Vegetables

On CRAN:

Conda:

2.26 score 1 stars 46 scripts 280 downloads 4 mentions 29 exports 10 dependencies

Last updated from:3a9ea3953d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK126
source / vignettesOK159
linux-release-x86_64OK127
macos-release-arm64OK158
macos-oldrel-arm64OK199
windows-develOK122
windows-releaseOK91
windows-oldrelOK136
wasm-releaseOK95

Exports:ACLUSTALRBARCACIplot_bivCLOSECLRDOTDUMMYFINDALRILRinvALRinvCLRinvSLRLRLR.VARLRAPCAPLOT.CAPLOT.LRAPLOT.PCAPLOT.RDAPLRRDASLRSTEPSTEPRVARWARD

Dependencies:caclusterellipselatticeMASSMatrixmgcvnlmepermutevegan