Computational phylogenetics reveal the history of sign languages

Sign languages are naturally occurring languages. As such, their emergence and spread reflect the histories of their communities. However, limitations in historical recordkeeping and linguistic documentation have hindered diachronic analysis of sign languages. Here, we use computational phylogenetic methods to study family structure among 19 sign languages from deaf communities worldwide. We use phonologically coded lexical data from contemporary languages to infer relatedness, and suggest these methods can help study regular form changes in sign languages. The inferred trees are consistent in key respects with known historical information, but challenge certain assumed groupings and surpass analyses made available by traditional methods. Moreover, the phylogenetic inferences are not reducible to geographic distribution, but do affirm the importance of geopolitical forces in the histories of human languages., Data set comprises coded vocabulary data from 19 sign languages. Vocabulary items were sourced from freely available online sign language dictionaries and were annotated using a web-based interface developed for the project. The categories and category values used in the coding system are compatible with and informed by leading contemporary theories of sign language phonology. Additional information about data collection and coding is available in Section 2 and Section 4 of the supplementary materials text., Proprietary and open-source software capable of working with .CSV files, including Microsoft Excel and Google Sheets., # Phylogenies with Matricial Datasets

This is the implementation of the numerical methods described in the paper \"Computational phylogenetics reveal histories of sign languages\". Instructions are given below to reproduce all the results from the paper, and to re-use the methodology on other data sets.

The code uses R version 4.1.2, with the \"parallel\" library. The code is also working on 4.1.2.

**All files and code are compressed in the ZIP (including a copy of the README); files are also available at **

## functions

This folder contains all the core functions needed to run the analysis. Users wishing to reproduce the results from the paper, or to make small changes (parameter values, different data) will not need to open these files. These files may be of use to researchers wishing to make more substantial changes to the model. In case of need, you can contact Grégoire Clarté.

## datasets

This folder contains the datasets used for the experiments presented in the papers. The dat...