@Inproceedings{conditschultz2019b,
  Author = {Nathaniel Condit-Schultz and Claire Arthur},
  Booktitle = {Proceedings of the International Society for Music Information Retrieval},
  Month = {November},
  Pages = {715–722},
  Title = {humdrumR: a New Take on an Old Approach to Computational Musicology},
  Year = {2019},
  Abstract = {Musicology research is a fundamentally humanistic endeavor. However, despite the productive work of a small niche of humanities-trained computational musicologists, most cutting-edge digital music research is pursued by scholars whose primary training is scientific or computational, not humanistic. This unfortunate situation is prolonged, at least in part, by the daunting barrier that computer coding presents to humanities scholars with no technical training. In this paper, we present humdrumR ("hum-drummer"), a software package designed to afford computational musicology research for both advanced and novice computer coders. Humdrum is a powerful and influential existing computational musicology framework, including the humdrum syntax—a flexible text data format with tens of thousands of extant scores available (Kern Scores)—and the Bash-based humdrum toolkit. HumdrumR is a modern replacement for the humdrum toolkit, based in the data-analysis/statistical programming language R. By combining the flexibility and transparency of the humdrum syntax with the powerful data analysis tools and concise syntax of R, humdrumR offers an appealing new approach to would-be computational musicologists. HumdrumR leverages R's powerful metaprogramming capabilities to create an extremely expressive and composable syntax, allowing novices to achieve usable analyses quickly while avoiding many coding concepts that are commonly challenging for beginners.},
  Doi = {10.5281/zenodo.3527910},
  Localfile = {PDFs/condit-schultz2019b.pdf}
}