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
Author: Greg Gloor
Maintainer: Greg Gloor <ggloor at>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: aIc results


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


Package source: aIc_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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