Statistical models of biased sampling in the form of 
  univariate and multivariate noncentral hypergeometric distributions, 
  including Wallenius' noncentral hypergeometric distribution and
  Fisher's noncentral hypergeometric distribution. 
  See vignette("UrnTheory") for explanation of these distributions.
  Literature: 
  Fog, A. (2008a). Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution, Communications in Statistics, Simulation and Computation, 37(2) <doi:10.1080/03610910701790269>.
  Fog, A. (2008b). Sampling methods for Wallenius’ and Fisher’s noncentral hypergeometric distributions, Communications in Statistics—Simulation and Computation, 37(2)
  <doi:10.1080/03610910701790236>.

Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.

Reading and writing BibTeX files using data frames in R sessions.

Tool for quantitative research in scientometrics and bibliometrics.
    It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and 
    Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>.
    'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS',
    'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' 
	(<https://www.dimensions.ai/>), 'OpenAlex' (<https://openalex.org/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>),  'Lens' (<https://lens.org>), 
	and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis 
    and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' (<https://scopus.com>) and 'Clarivate Analytics Web of Science' (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package 'bibliometrix' (<https://bibliometrix.org>).

Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.