Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.

Provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

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.