Provides users with an EZ-to-use platform for representing data
             with biplots. Currently principal component analysis (PCA), canonical variate
             analysis (CVA) and simple correspondence analysis (CA) biplots are included. 
             This is accompanied by various formatting
             options for the samples and axes. Alpha-bags and concentration ellipses
             are included for visual enhancements and interpretation. For an extensive
             discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J.
             (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.

Provides a GUI with which users can construct and interact
        with biplots.

Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>. Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix).

Bayesian models for accurately estimating conditional 
    distributions by race, using Bayesian Improved Surname Geocoding (BISG) 
    probability estimates of individual race. Implements the methods described
    in McCartan, Fisher, Goldin, Ho and Imai (2025) <doi:10.1080/01621459.2025.2526695>.

Use 'BirdNET', a state-of-the-art deep learning classifier, to automatically identify (bird) sounds.
    Analyze bioacoustic datasets without any computer science background using a pre-trained model or a custom trained classifier.
    Predict bird species occurrence based on location and week of the year.
    Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) <doi:10.1016/j.ecoinf.2021.101236>.