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LBBNN  

Latent Binary Bayesian Neural Networks Using 'torch'
View on CRAN: Click here


Download and install LBBNN package within the R console
Install from CRAN:
install.packages("LBBNN")

Install from Github:
library("remotes")
install_github("cran/LBBNN")

Install by package version:
library("remotes")
install_version("LBBNN", "0.1.4")



Attach the package and use:
library("LBBNN")
Maintained by
Lars Skaaret-Lund
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-12-01
Latest Update: 2025-12-01
Description:
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>.
How to cite:
Lars Skaaret-Lund (2025). LBBNN: Latent Binary Bayesian Neural Networks Using 'torch'. R package version 0.1.4, https://cran.r-project.org/web/packages/LBBNN. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.1.1 (2025-12-01 15:10), 0.1.2 (2025-12-10 10:50), 0.1.3 (2026-01-07 22:20), 0.1.4 (2026-01-12 16:30)
Other packages that cited LBBNN R package
View LBBNN citation profile
Other R packages that LBBNN depends, imports, suggests or enhances
Complete documentation for LBBNN
Functions, R codes and Examples using the LBBNN R package
Full LBBNN package functions and examples
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