The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Update: CoDaCoRe is now live on CRAN
A self-contained, up-to-date implementation of CoDaCoRe, in the R programming language, by the original authors.
The CoDaCoRe guide contains a detailed tutorial on installation, usage and functionality.
Note this repository is under active development. If you would like to use CoDaCoRe on your dataset, and have any questions regarding the installation, usage, implementation, or model itself, do not hesitate to contact eg2912@columbia.edu. Some previously asked questions are available on the Issues page. Contributions, fixes, and feature requests are also welcome - please create an issue, submit a pull request, or email me.
install.packages('codacore')
library("codacore")
help(codacore) # if in doubt, check documentation
data("Crohn") # load some data and apply codacore
<- Crohn[, -ncol(Crohn)] + 1
x <- Crohn[, ncol(Crohn)]
y = codacore(
model # compositional input, e.g., HTS count data
x, # response variable, typically a 0/1 binary indicator
y, logRatioType = "balances", # can use "amalgamations" instead, or abbreviations "B" and "A"
lambda = 1 # regularization strength (default corresponds to 1SE rule)
)print(model)
plot(model)
Gordon-Rodriguez, Elliott, Thomas P. Quinn, and John P. Cunningham. “Learning sparse log-ratios for high-throughput sequencing data.” Bioinformatics 38.1 (2022): 157-163. [link]
Quinn, Thomas P., Elliott Gordon-Rodriguez, and Ionas Erb. “A critique of differential abundance analysis, and advocacy for an alternative.” arXiv preprint arXiv:2104.07266 (2021). [link]
Thanks for your contributions to codacore!
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.