LBBNN: Latent Binary Bayesian Neural Networks Using 'torch'
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: |
0.1.1 |
| Depends: |
R (≥ 3.5) |
| Imports: |
ggplot2, torch, igraph, coro, svglite |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2025-12-01 |
| DOI: |
10.32614/CRAN.package.LBBNN (may not be active yet) |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=LBBNN
to link to this page.