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LBBNN
View on CRAN: Click
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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
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[Scholar Profile | Author Map]
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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 26 Jun. 2026.
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