Systematic review, meta-analysis, and other forms of evidence synthesis require researchers to identify the body of evidence relevant to their research question. Partially automating evidence synthesis would reduce the amount of human time and effort needed to conduct a systematic review and reduce bias in keyword selection when researchers develop search strategies. This package facilitates quick, objective, reproducible search strategy development using text-mining and keyword co-occurrence networks to identify important terms to include in a search strategy as described in Grames et al. (2019) <doi:10.1111/2041-210X.13268>. It can automatically write Boolean search strings in up to 53 different languages, with stemming support for English. To assess the quality of a search, it can also check the results of a search against a set of known, relevant articles to get performance metrics.
Package details |
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Maintainer | |
License | GPL-3 |
Version | 1.0.0 |
URL | http://elizagrames.github.io/litsearchr |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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