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>.

A suite of tools for literature-based discovery in biomedical research. 
    Provides functions for retrieving scientific articles from 'PubMed' and 
    other NCBI databases, extracting biomedical entities (diseases, drugs, genes, etc.), 
    building co-occurrence networks, and applying various discovery models 
    including 'ABC', 'AnC', 'LSI', and 'BITOLA'. The package also includes 
    visualization tools for exploring discovered connections.

A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.

Interfacing to Nocedal et al. L-BFGS-B.3.0
        (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>)
        limited memory BFGS minimizer with bounds on parameters.
        This is a fork of 'lbfgsb3'.
	This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same
	function types and optimization as the optim() function (see writing 'R' extensions
        and source for details).  This package also adds more stopping criteria as well
        as allowing the adjustment of more tolerances.