sillywalk
Library for maximum likelihood principal component analysis for AnyBody models
Library for maximum likelihood principal component analysis for AnyBody models
To install this package, run one of the following:
sillywalk is a Python library for statistical modeling of human motion and anthropometric data with the AnyBody Modeling System. It implements Maximum Likelihood Principal Component Analysis (ML‑PCA) to learn compact, low‑dimensional models from datasets, predict missing or individualized signals from partial inputs, and export those predictions as AnyScript include files that plug directly into AnyBody models.
Summary
Library for maximum likelihood principal component analysis for AnyBody models
Last Updated
Dec 28, 2025 at 22:33
License
MIT
Total Downloads
167
Supported Platforms
GitHub Repository
https://github.com/AnyBody-Research-Group/sillywalkDocumentation
https://github.com/AnyBody-Research-Group/sillywalk