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aIc: Testing for Compositional Pathologies in Datasets

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).

Version: 1.0
Depends: R (≥ 3.5.0)
Imports: matrixcalc, zCompositions, shiny, edgeR, ALDEx2, vegan
Suggests: BiocStyle, knitr, rmarkdown
Published: 2022-10-04
DOI: 10.32614/CRAN.package.aIc
Author: Greg Gloor
Maintainer: Greg Gloor <ggloor at uwo.ca>
BugReports: https://github.com/ggloor/aIc/issues
License: GPL (≥ 3)
URL: https://github.com/ggloor/aIc
NeedsCompilation: no
CRAN checks: aIc results

Documentation:

Reference manual: aIc.pdf
Vignettes: aIc: am I compositional?

Downloads:

Package source: aIc_1.0.tar.gz
Windows binaries: r-devel: aIc_1.0.zip, r-release: aIc_1.0.zip, r-oldrel: aIc_1.0.zip
macOS binaries: r-release (arm64): aIc_1.0.tgz, r-oldrel (arm64): aIc_1.0.tgz, r-release (x86_64): aIc_1.0.tgz, r-oldrel (x86_64): aIc_1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=aIc to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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