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).

Create attractive palettes based on the colors of the world's birds. Palettes are composed of 2 to 9 colors, with options to expand palettes via interpolation. Compatible with the package 'ggplot2' and base R graphics.

Provides a unified set of methods to detect scientific emergence
    and technological trajectories in academic papers and patents. The package
    combines citation network analysis with community detection and attribute
    extraction, also applying natural language processing (NLP) and structural
    topic modeling (STM) to uncover the contents of research communities. It
    implements metrics and visualizations of community trajectories, including
    novelty indicators, citation cycle time, and main path analysis, allowing
    researchers to map and interpret the dynamics of emerging knowledge fields.
    Applications of the method include: Souza et al. (2022)
    <doi:10.1002/bbb.2441>, Souza et al. (2022)
    <doi:10.14211/ibjesb.e1742>, Matos et al. (2023)
    <doi:10.1007/s43938-023-00036-3>, Maria et al. (2023)
    <doi:10.3390/su15020967>, Biazatti et al. (2024)
    <doi:10.1016/j.envdev.2024.101074>, Felizardo et al. (2025)
    <doi:10.1007/s12649-025-03136-z>, and Miranda et al. (2025)
    <doi:10.1016/j.ijhydene.2025.01.089>.