Package: banocc 1.29.0

George Weingart

banocc: Bayesian ANalysis Of Compositional Covariance

BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element.

Authors:Emma Schwager [aut, cre], Curtis Huttenhower [aut]

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# Install 'banocc' in R:

install.packages('banocc', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

compositions_hard_null - Simulated compositional data with no feature correlations
compositions_neg_spike - Simulated compositional data with a negative count correlation
compositions_null - Simulated compositional data with no feature correlations
compositions_pos_spike - Simulated compositional data with a positive count correlation
counts_hard_null - Simulated count data with no feature correlations
counts_neg_spike - Simulated count data with one negative feature correlation
counts_null - Simulated count data with no feature correlations
counts_pos_spike - Simulated count data with one positive feature correlation

On BioConductor:banocc-1.29.0(bioc 3.20)banocc-1.28.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

3 exports 0.91 score 56 dependencies

Last updated 2 months agofrom:3a8575fb4d

Exports:banocc_model get_banocc_output run_banocc

Dependencies:abind backports BH callr checkmate cli coda colorspace desc distributional fansi farver generics ggplot2 glue gridExtra gtable inline isoband labeling lattice lifecycle loo magrittr MASS Matrix matrixStats mgcv munsell mvtnorm nlme numDeriv pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan scales StanHeaders stringi stringr tensorA tibble utf8 vctrs viridisLite withr

Introduction to BAnOCC (Bayesian Analaysis Of Compositional Covariance)

Emma Schwager

Rendered frombanocc-vignette.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2022-04-20
Started: 2017-03-31

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
banocc: A package for Bayesian ANalysis of Compositional Correlation	banocc-package banocc
The stan model used in the Bayesian fit	banocc_model
Simulated compositional data with no feature correlations	compositions_hard_null
Simulated compositional data with a negative count correlation	compositions_neg_spike
Simulated compositional data with no feature correlations	compositions_null
Simulated compositional data with a positive count correlation	compositions_pos_spike
Simulated count data with no feature correlations	counts_hard_null
Simulated count data with one negative feature correlation	counts_neg_spike
Simulated count data with no feature correlations	counts_null
Simulated count data with one positive feature correlation	counts_pos_spike
Takes a model fit from BAnOCC, evaluates convergence and generates appropriate convergence metrics and inference	get_banocc_output
Runs BAnOCC to fit the model and generate appropriate convergence metrics and inference.	run_banocc