historical-linguistics

What problem does it solve?

Historical linguistics work is slow when you must manually compare multilingual wordlists, identify cognates, estimate relationships, and build phylogenetic subgrouping from basic-vocabulary data.

Core Features & Use Cases

• Cognate and sound correspondence detection: Use edit-distance and alignment approaches (or LingPy workflows) to cluster candidate cognates and derive correspondence patterns.
• Lexicostatistics and divergence-time estimation: Compute percent shared cognates from Swadesh lists and estimate time depth using a retention-rate model.
• Phylogenetic tree inference: Build a language distance matrix and generate dendrograms using hierarchical clustering (e.g., UPGMA-style linkage).

Use case examples: subgroup related languages from Swadesh data, test whether two language varieties share systematic sound changes, or produce a distance heatmap and a tree for a research report.

Quick Start

Use the historical-linguistics skill to compute a cognate-based distance matrix and infer a phylogenetic tree from a Swadesh wordlist dataset.