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aIc  

Testing for Compositional Pathologies in Datasets
View on CRAN: Click here


Download and install aIc package within the R console
Install from CRAN:
install.packages("aIc")

Install from Github:
library("remotes")
install_github("cran/aIc")

Install by package version:
library("remotes")
install_version("aIc", "1.0")



Attach the package and use:
library("aIc")
Maintained by
Greg Gloor
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-04
Latest Update: 2022-10-04
Description:
A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) ), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) ) 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) , Anders et al. (2013)).
How to cite:
Greg Gloor (2022). aIc: Testing for Compositional Pathologies in Datasets. R package version 1.0, https://cran.r-project.org/web/packages/aIc. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for aIc
Functions, R codes and Examples using the aIc R package
Some associated functions: aIc.coherent . aIc.dominant . aIc.perturb . aIc.plot . aIc.runExample . aIc.scale . aIc.singular . meta16S . metaTscome . selex . singleCell . transcriptome . 
Some associated R codes: aIc.R . aIc.plot.R . aId.R . aIs.R . aIsc.R . normalize.R .  Full aIc package functions and examples
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