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Linguistic reconstruction with LingPy

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# LingRex: Linguistic Reconstruction with LingPy

[![Build Status](https://github.com/lingpy/lingrex/workflows/tests/badge.svg)](https://github.com/lingpy/lingrex/actions?query=workflow%3Atests) [![codecov.io](http://codecov.io/github/lingpy/lingrex/coverage.svg?branch=master)](http://codecov.io/github/lingpy/lingrex?branch=master) [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.1544943.svg)](https://doi.org/10.5281/zenodo.1544943) [![PyPI version](https://badge.fury.io/py/lingrex.png)](https://badge.fury.io/py/lingrex)

LingRex offers the code needed for the automatic inference of sound correspondence patterns as described in the following paper:

> List, J.-M. (2019): Automatic inference of sound correspondence patterns across multiple languages. Computational Linguistics 45.1. 137-161. [DOI: 10.1162/coli_a_00344](https://doi.org/10.1162/coli_a_00344)

To test this workflow, please check the workflow code example in tests/workflows/list-2019.

When using this package in your research, please make sure to quote the paper accordingly, and quote the software package as follows:

> List, Johann-Mattis and Forkel, Robert (2021): LingRex: Linguistic Reconstruction with LingPy. [Computer software, Version 1.1.1]. Geneva: Zenodo. [DOI: 10.5281/zenodo.1544943](https://doi.org/10.5281/zenodo.1544943)

Since this software package itself makes use of LingPy’s alignment algorithms, you should also quote the LingPy package itself.

> List, J.-M. and R. Forkel (2021): LingPy. A Python library for quantitative tasks in historical linguistics. Version 2.6.7. Version 2.6.7. Max Planck Institute for Evolutionary Anthropology: Leipzig. https://lingpy.org

## Installation

Install the package via pip:

`shell pip install lingrex `

## Further Examples

The borrowing detection algorithm implemented in LingRex is introduced in the paper:

> List, J.-M. and R. Forkel (2021): Automated identification of borrowings in multilingual wordlists [version 1; peer review: 3 approved, 1 approved with reservations]. Open Research Europe 1.79. 1-11. [DOI: 10.12688/openreseurope.13843.1](https://doi.org/10.12688/openreseurope.13843.1)

If you use this algorithm, please cite LingRex and this paper.

In addition to the paper in which the correspondence pattern inference algorithm was first introduced, LingRex also offers the code to compute the workflow described in the following paper:

> Wu, M.-S., N. Schweikhard, T. Bodt, N. Hill, and J.-M. List (2020): Computer-Assisted Language Comparison. State of the Art. Journal of Open Humanities Data 6.2. 1-14. [DOI: 10.5334/johd.12](https://doi.org/10.5334/johd.12)

To test this workflow, please check the workflow code example in tests/workflows/wu-2020.

If you use this workflow in your work, please quote this paper as well.

In addition, our experiment (with T. Bodt) on predicting words with the help of sound correspondence patterns also made use of the LingRex package.

> Bodt, T. and J.-M. List (2021): Reflex prediction. A case study of Western Kho-Bwa. Diachronica 0.0. 1-38. [DOI: 10.1075/dia.20009.bod](https://doi.org/10.1075/dia.20009.bod)

To test this workflow, please check the workflow code example in tests/workflows/bodt-2019.

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