Package: bnns 0.1.2.9000
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:
bnns_0.1.2.9000.tar.gz
bnns_0.1.2.9000.zip(r-4.5)bnns_0.1.2.9000.zip(r-4.4)bnns_0.1.2.9000.zip(r-4.3)
bnns_0.1.2.9000.tgz(r-4.4-any)bnns_0.1.2.9000.tgz(r-4.3-any)
bnns_0.1.2.9000.tar.gz(r-4.5-noble)bnns_0.1.2.9000.tar.gz(r-4.4-noble)
bnns_0.1.2.9000.tgz(r-4.4-emscripten)bnns_0.1.2.9000.tgz(r-4.3-emscripten)
bnns.pdf |bnns.html✨
bnns/json (API)
NEWS
# 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 site:https://swarnendu-stat.github.io
Last updated 8 hours agofrom:72e14b4179. Checks:1 OK, 6 NOTE. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Jan 14 2025 |
R-4.5-win | NOTE | Jan 14 2025 |
R-4.5-linux | NOTE | Jan 14 2025 |
R-4.4-win | NOTE | Jan 14 2025 |
R-4.4-mac | NOTE | Jan 14 2025 |
R-4.3-win | NOTE | Jan 14 2025 |
R-4.3-mac | NOTE | Jan 14 2025 |
Exports:bnnsbnns_traingenerate_stan_codegenerate_stan_code_bingenerate_stan_code_catgenerate_stan_code_contmeasure_binmeasure_catmeasure_contrelusigmoidsoftmax_3dsoftplus
Dependencies:abindbackportsBHcallrcheckmateclicolorspacedescdistributionalfansifarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigplyrposteriorpROCprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanscalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Generic Function for Fitting Bayesian Neural Network Models | bnns |
Bayesian Neural Network Model Using Formula(default) Interface | bnns.default |
Measure Performance for Binary Classification Models | measure_bin |
Measure Performance for Multi-Class Classification Models | measure_cat |
Measure Performance for Continuous Response Models | measure_cont |
Predict Method for '"bnns"' Objects | predict.bnns |
Print Method for '"bnns"' Objects | print.bnns |
relu transformation | relu |
sigmoid transformation | sigmoid |
Apply Softmax Function to a 3D Array | softmax_3d |
softplus transformation | softplus |
Summary of a Bayesian Neural Network (BNN) Model | summary.bnns |