Preprocessing tools and biodiversity measures 
    (species abundance, species richness, population heterogeneity and 
    sensitivity) for analysing marine benthic data. See Van Loon et al.
    (2015) <doi:10.1016/j.seares.2015.05.002> for an application of 
    these tools.

Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

Bayesian analysis for exponential random graph models using
    advanced computational algorithms. More information can be found at:
    <https://acaimo.github.io/Bergm/>.

A suite of open-source R functions designed to produce standard 
    metrics for forest management and ecology from forest inventory data. The 
    overarching goal is to minimize potential inconsistencies introduced by the 
    algorithms used to compute and summarize core forest metrics. Learn more 
    about the purpose of the package and the specific algorithms used in the 
    package at <https://github.com/kearutherford/BerkeleyForestsAnalytics>.

Bayesian analysis for stochastic extensions of non-linear
    dynamic systems using advanced computational algorithms. Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) <doi:10.48550/arXiv.2211.15229>.

Draw horizontal histograms, color scattered points by 3rd dimension,
    enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, 
    fit multiple functions.

Interface to the Python package 'BERTopic' <https://maartengr.github.io/BERTopic/index.html> for transformer-based topic modeling. Provides R wrappers to fit BERTopic models, transform new documents, update and reduce topics, extract topic- and document-level information, and generate interactive visualizations. 'Python' backends and dependencies are managed via the 'reticulate' package.