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

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