jsilve24/TeacupCerberus: Bayesian Estimation of Covariation Between and Within Two Multivariate Count Datasets
Estimates covariation within and between two count datasets where the counts contain multinomial variation (e.g., sequence count data like microbiome 16S or bulk/single-cell RNA-seq). The model outputs Bayesian posterior samples over covariance matricies. The entire posterior reflects uncertainty in the true covariation due to multinomial counting.
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Package details |
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| Maintainer | Justin Silverman <Justin.Silverman@duke.edu> |
| License | GPL-2 | GPL-3 |
| Version | 0.1.1 |
| URL | https://github.com/jsilve24/TeacupCerberus https://jsilve24.github.io/TeacupCerberus/index.html |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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