Package: bnns 1.0.0.9000

bnns: Bayesian Neural Network with 'Stan'

Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by 'Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) <doi:10.1007/978-1-4612-0745-0>.

Authors:Swarnendu Chatterjee [aut, cre, cph]

bnns_1.0.0.9000.tar.gz
bnns_1.0.0.9000.zip(r-4.7)bnns_1.0.0.9000.zip(r-4.6)bnns_1.0.0.9000.zip(r-4.5)
bnns_1.0.0.9000.tgz(r-4.6-any)bnns_1.0.0.9000.tgz(r-4.5-any)
bnns_1.0.0.9000.tar.gz(r-4.7-any)bnns_1.0.0.9000.tar.gz(r-4.6-any)
bnns_1.0.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bnns/json (API)

# Install 'bnns' in R:
install.packages('bnns', repos = c('https://swarnendu-stat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/swarnendu-stat/bnns/issues

Pkgdown/docs site:https://swarnendu-stat.github.io

On CRAN:

Conda:

5.76 score 4 stars 72 scripts 284 downloads 18 exports 49 dependencies

Last updated from:36d5a9493d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK191
source / vignettesOK290
linux-release-x86_64OK197
macos-release-arm64OK163
macos-oldrel-arm64OK170
windows-develOK140
windows-releaseOK155
windows-oldrelOK171
wasm-releaseOK156

Exports:act_fnbnnsbnns_trainchainsiterLload_bnnsmeasure_binmeasure_catmeasure_contnodesopencl_diagnosticsrelusave_bnnssigmoidsoftmax_3dsoftpluswarmup

Dependencies:abindbackportsBHcallrcheckmateclicpp11descdigestdistributionalfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglifecycleloomagrittrmatrixStatsnumDerivotelpillarpkgbuildpkgconfigposteriorpROCprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanS7scalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr

bnns
Overview | 1. Installation | 2. Preparing the Data | 3. Fitting a Bayesian Neural Network Model | Regression Example | Binary Classification Example | Multiclass Classification Example | 4. Summarizing the Model | 5. Making Predictions | 6. Evaluating the Model | Regression Evaluation | Binary Classification Evaluation | Multiclass Classification Evaluation | 7. Customized Prior | 8. Notes on Bayesian Neural Networks | References
Rendered frombnns.Rmdusingknitr::rmarkdown

Last update: 2026-06-07
Started: 2024-12-23

Using bnns with tidymodels
Introduction | Setup | Regression | 1. Specify the Model | 2. Create a Workflow and Fit | 3. Predict | Classification | 3. Predict Classes and Probabilities
Rendered fromtidymodels.Rmdusingknitr::rmarkdown

Last update: 2026-06-04
Started: 2026-06-04