typology-wals

Test implicational universals with WALS data using Fisher's exact test and Yule's Q.

33|6|Updated Mar 17, 2026

Search Tags:#linguistics #python analysis #typology #wals #implicational universals #word order #geospatial mapping

Ready to run

npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill typology-wals

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: typology-wals
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/12-linguistics/typology-wals
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill typology-wals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, matplotlib, geopandas.

What problem does it solve?

This Skill helps you analyze cross-linguistic typological patterns from WALS to test implicational universals and understand how features relate to word order and geography.

Core Features & Use Cases

Cross-linguistic feature analysis: Compare typological feature co-occurrence patterns using WALS-style structured data.
Implicational universal testing: Evaluate Greenbergian/Greenberg-inspired universals with contingency tables, Fisher’s exact tests, and Yule’s Q.
Typological mapping: Create geographic distribution maps (via GeoPandas when available, otherwise scatter fallbacks) to visualize where feature values cluster.
Use Case: Determine whether SOV languages are statistically associated with postpositions and visualize where SOV and tone clusters appear on a world map.

Quick Start

Use the typology-wals skill to test whether SOV predicts postpositions in a WALS-derived dataset and produce a mapped summary of the relevant feature distributions.

Frequently Asked Questions about typology-wals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I test implicational universals using WALS data in Python?▼

To test implicational universals with WALS data, load structured typological tables into Python and compute contingency-based metrics like conditional probability, Fisher's exact test, and Yule's Q to quantify feature co-occurrence across languages.

What statistical methods are used to quantify linguistic feature co-occurrence in typology?▼

Quantifying linguistic feature co-occurrence in typology involves computing contingency-based metrics such as conditional probability, Fisher's exact test, and Yule's Q to evaluate the strength and significance of Greenbergian-style implicational universals.

Can I map the geographic distribution of word order typology features with Python?▼

You can map the geographic distribution of word order typology features using Python by generating maps with GeoPandas, or utilizing Matplotlib scatter fallbacks, to visualize where specific feature values like SOV clusters appear on a world map.

Does this analysis approach work with WALS data to determine if SOV predicts postpositions?▼

This approach works with WALS data to determine if SOV predicts postpositions by constructing contingency tables from the typological dataset and applying Fisher's exact test to evaluate the statistical significance of the feature association.

What is the best way to visualize cross-linguistic typological patterns from WALS?▼

The best way to visualize cross-linguistic typological patterns from WALS is using Python with Pandas for data handling, GeoPandas for geographic distribution maps, and Matplotlib to generate scatter plots that illustrate feature co-occurrence clusters.

Do I need GeoPandas to generate typological feature distribution maps?▼

You do not need GeoPandas to generate typological feature distribution maps, as the analysis supports a scatter plot fallback using Matplotlib when GeoPandas is unavailable, ensuring geographic visualization of feature clusters remains possible.