Create Different Types of Bibliometric Networks
c(
person(given = "Aurélien",
family = "Goutsmedt",
role = c("cre","aut"),
email = "agoutsmedt@hotmail.fr",
comment = c(ORCID = "0000-0002-3788-7237")),
person(given = "François",
family = "Claveau",
role = c("aut"),
email = "françois.claveau@usherbrooke.ca",
comment = c(ORCID = "0000-0001-7129-7794")),
person(given = "Alexandre",
family = "Truc",
role = c("aut"),
email = "alexandre.truc77@gmail.com",
comment = c(ORCID = "0000-0002-1328-7819")))
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>.
Aurélien Goutsmedt [cre, aut] (<https://orcid.org/0000-0002-3788-7237>),
François Claveau [aut] (<https://orcid.org/0000-0001-7129-7794>),
Alexandre Truc [aut] (<https://orcid.org/0000-0002-1328-7819>)

Reading Mass Spectrometric Search Results
Christian Panse <cp@fgcz.ethz.ch>, Witold E. Wolski <wew@fgcz.ethz.ch>
R class to access 'sqlite', 'BiblioSpec' generated, mass spectrometry search result files,
containing detailed information about peptide spectra matches.
Convert 'Mascot' '.dat' or e.g. 'comet' '.pep.xml' files with 'BiblioSpec' into 'sqlite' files and than
access them with the 'CRAN' 'bibliospec' package to analyse with the R-packages 'specL' to generate
spectra libraries, 'protViz' to annotate spectra, or 'prozor' for false discovery rate
estimation and protein inference.