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-any)easyCODA_0.40.2.zip(r-4.6-any)easyCODA_0.40.2.zip(r-4.5-any)
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✨
DESCRIPTION
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 411 downloads 4 mentions 29 exports 10 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK139
source / vignettesOK196
linux-releaseOK142
macos-releaseOK142
macos-oldrelOK166
windows-develOK68
windows-releaseOK84
windows-oldrelOK82
wasm-releaseOK77

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

Dependencies:caclusterellipselatticeMASSMatrixmgcvnlmepermutevegan