R

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.

In this post, you will learn how to scrape various research documents from the [Bank of England](https://www.bankofengland.co.uk/) website.

In this post, you will learn how to scrape various research documents from the [European Central Bank](https://www.ecb.europa.eu/home/html/index.en.html) website.

In this post, you will learn how to scrape speeches from the [Bank of International Settlements database](https://www.bis.org/cbspeeches/index.htm?m=256) which gathers most of central bankers' speeches in English.

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.

In this post, you will learn how to extract data from Dimensions website and how to clean them. These data allow you to build bibliographic networks.

In this post, you will learn how to extract data from Scopus website or with Scopus APIs and how to clean the data extracted from Scopus website. These data allow you to build bibliographic networks.

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.