Imports, constructs, and exports bibliometric networks from
    scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX',
    'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref'
    exports. Goes beyond standard co-networks with attention-weighted
    networks (lead, last, proximity, circular position weights),
    position-aware counting (harmonic, arithmetic, geometric,
    golden-ratio), similarity and dissimilarity normalisations,
    temporal networks with fixed, sliding, and cumulative windows,
    disparity-filter backbone extraction, historiograph construction,
    and local citation scoring. Methods described in López-Pernas,
    Saqr & Apiola (2023) <doi:10.1007/978-3-031-25336-2_5>.

Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see <http://crexplorer.net>). Further extension to more plot variants is planned.

A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j.  The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.

Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.