RcppCoDA: TeacupCerberus
In jsilve24/TeacupCerberus: Bayesian Estimation of Covariation Between and Within Two Multivariate Count Datasets
Description
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.
Author(s)
Justin D Silverman
jsilve24/TeacupCerberus documentation built on Nov. 4, 2019, 3:25 p.m.
R Package Documentation
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Description
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.
Author(s)
Justin D Silverman
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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