Latent binary Bayesian neural networks (LBBNNs) are implemented using 'torch', an R interface to the LibTorch backend. Supports mean-field variational inference as well as flexible variational posteriors using normalizing flows. The standard LBBNN implementation follows Hubin and Storvik (2024) <doi:10.3390/math12060788>, using the local reparametrization trick as in Skaaret-Lund et al. (2024) <https://openreview.net/pdf?id=d6kqUKzG3V>. Input-skip connections are also supported, as described in Høyheim et al. (2025) <doi:10.48550/arXiv.2503.10496>.
| Version: | 0.1.4 |
| Depends: | R (≥ 3.5) |
| Imports: | ggplot2, torch, igraph, coro, svglite |
| Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown, torchvision |
| Published: | 2026-01-12 |
| DOI: | 10.32614/CRAN.package.LBBNN |
| Author: | Lars Skaaret-Lund [aut, cre], Aliaksandr Hubin [aut], Eirik Høyheim [aut] |
| Maintainer: | Lars Skaaret-Lund <lars.skaaret-lund at nmbu.no> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README, NEWS |
| CRAN checks: | LBBNN results |
| Reference manual: | LBBNN.html , LBBNN.pdf |
| Vignettes: |
Getting started with LBBNN (source, R code) |
| Package source: | LBBNN_0.1.4.tar.gz |
| Windows binaries: | r-devel: LBBNN_0.1.4.zip, r-release: LBBNN_0.1.4.zip, r-oldrel: LBBNN_0.1.4.zip |
| macOS binaries: | r-release (arm64): LBBNN_0.1.4.tgz, r-oldrel (arm64): LBBNN_0.1.4.tgz, r-release (x86_64): LBBNN_0.1.4.tgz, r-oldrel (x86_64): LBBNN_0.1.4.tgz |
| Old sources: | LBBNN archive |
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