Introduction | How To Install | From Within R | From Bitbucket (Compressed File) | From Bitbucket (Directly) | How To Run | Loading | Package Features | Data and Prior Input | Required Input | Hyperparameters | Sampling Control | General Sampling Control | Number of Cores | Initial Values | Output Control | Credible Interval Width | Checking Convergence | Additional Output | Assessing Convergence | Traceplots | Rhat Statistics | Choosing Priors | Log-Basis Precision Matrix | Log-Basis Mean | GLASSO Shrinkage Parameter | The Model | References
Package: banocc 1.37.0
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:
banocc_1.37.0.tar.gz
banocc_1.37.0.zip(r-4.7)banocc_1.37.0.zip(r-4.6)banocc_1.37.0.zip(r-4.5)
banocc_1.37.0.tgz(r-4.6-any)banocc_1.37.0.tgz(r-4.5-any)
banocc_1.37.0.tar.gz(r-4.7-any)banocc_1.37.0.tar.gz(r-4.6-any)
banocc_1.37.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
banocc/json (API)
| # 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-1.37.0(bioc 3.24)banocc-1.36.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
immunooncologymetagenomicssoftwarebayesian
Last updated from:1f631fc2dc. Checks:1 WARNING, 5 ERROR, 2 OK, 2 NOTE. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | WARNING | 189 | ||
| linux-devel-x86_64 | ERROR | 279 | ||
| source / vignettes | OK | 287 | ||
| linux-release-x86_64 | ERROR | 285 | ||
| macos-release-arm64 | NOTE | 163 | ||
| macos-oldrel-arm64 | NOTE | 129 | ||
| windows-devel | ERROR | 207 | ||
| windows-release | ERROR | 219 | ||
| windows-oldrel | ERROR | 212 | ||
| wasm-release | OK | 174 |
Exports:banocc_modelget_banocc_outputrun_banocc
Dependencies:abindbackportsBHcallrcheckmateclicodacpp11descdistributionalfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrmatrixStatsmvtnormnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanS7scalesStanHeadersstringistringrtensorAtibbleutf8vctrsviridisLitewithr
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 |
