Na minha lista:
| Autor principal: | |
|---|---|
| Formato: | Recurso digital |
| Idioma: | |
| Publicado em: |
Zenodo
2026
|
| Acesso em linha: | https://doi.org/10.5281/zenodo.19225731 |
| Tags: |
Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
|
Sumário:
- <h2>LatticeFit v0.2.0</h2> <p>Python package for detecting and validating discrete multiplicative structure in empirical data.</p> <h3>What's included</h3> <ul> <li>Core fitting: <code>fit()</code>, <code>discover()</code>, <code>FitResult</code></li> <li>Statistical validation: log-uniform and sector-anchor null tests</li> <li>Bootstrap confidence intervals: <code>bootstrap_ci()</code></li> <li>AIC/BIC model selection: <code>select_model()</code></li> <li>Publication bundle generator: <code>generate_bundle()</code></li> <li>CLI: <code>latticefit data.csv --auto --null 50000</code></li> <li>4 built-in demos: SM masses, earthquakes, musical notes, mammal masses</li> <li>Validated across 18 real-world datasets</li> </ul> <h3>Installation</h3> <p>pip install latticefit</p> <h3>Patent</h3> <p>US provisional patent No. 64/013,306 covers the sector-anchor null test. All other functionality is MIT licensed.</p>