I am thrilled to announce the inaugural development release of [Ranystyle](https://agoutsmedt.github.io/Ranystyle/index.html)! Ranystyle is designed to extract, parse and clean bibliographic references from an array of sources including PDFs, text documents, and references stored in a R objects. At its core, [Ranystyle](https://agoutsmedt.github.io/Ranystyle/index.html) harnesses the power of the [anystyle](https://anystyle.io/) Ruby gem, wrapping (and extending) its capabilities within an intuitive R interface.
Ranystyle (pronounce R-anystyle) is an R package designed to automate the extraction, parsing, and cleaning of bibliographic references from PDF and text documents as well as vector of references stored in an R object. Utilizing the power of the ‘anystyle’ Ruby gem, it segments references and converts them into structured formats suitable for analysis and use.
A github repository that gathers different scripts to extract (mannually or via APIs) and then clean bibliometric data.
When the temptation is growing in you to try your hand at quantitative methods, the first question is likely to be "but how can I do, and which tools should I learn to use?" I give here some arguments to engage yourself in learning R and then present different tutorials and R packages useful for historians of economics.
I am very pleased to announce the initial release of biblionetwork to CRAN! biblionetwork is designed to build easily and quickly large list of edges for bibliometric networks. You can identify the edges for different types of network (bibliometric coupling or co-citation, or co-authorship networks) and use different methods to calculate the weights of edges.
The biblionetwork package provides functions to create fastly bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network.
The networkflow package proposes functions to make it easier and quicker to work on networks. It mainly targets working on bibliometric networks. This package heavily relies on [igraph](https://igraph.org/r/) and [tidygraph](https://tidygraph.data-imaginist.com/index.html), and aims at producing ready-made networks for projecting them using [ggraph](https://ggraph.data-imaginist.com/).