The latest articles on DEV Community by Nischal Kafle (@nischal_kafle_ef7237c4019). https://dev.to/nischal_kafle_ef7237c4019 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3463404%2F38425117-4725-43f0-897e-a76db4e2c32c.png https://dev.to/nischal_kafle_ef7237c4019 en Nischal Kafle Wed, 27 Aug 2025 18:32:51 +0000 https://dev.to/nischal_kafle_ef7237c4019/a-python-library-to-model-stationary-and-non-stationary-extreme-value-distributions-gev-gpd-376c https://dev.to/nischal_kafle_ef7237c4019/a-python-library-to-model-stationary-and-non-stationary-extreme-value-distributions-gev-gpd-376c <h1> nsEVDx </h1> <p><strong>A Python library to model stationary and non-stationary extreme value distributions (GEV &amp; GPD).</strong></p> <ul> <li><a href="https://pypi.org/project/nsEVDx/" rel="noopener noreferrer">PyPI</a></li> <li><a href="https://github.com/Nischalcs50/nsEVDx" rel="noopener noreferrer">GitHub</a></li> </ul> <h2> Key Features </h2> <ul> <li>Stationary and non-stationary <code>GEV</code> / <code>GPD</code> models</li> <li>Arbitrary covariates in location, scale, and shape parameters</li> <li>Supports both Bayesian and Frequentist approaches</li> <li>Transparent, fully customizable <code>MCMC</code> engine implemented in NumPy</li> <li>Advanced samplers: Metropolis-Hastings, MALA, and HMC</li> <li>Minimal dependencies (<code>NumPy</code>, <code>SciPy</code>, <code>matplotlib</code>)</li> <li><p>Diagnostics: trace plots, acceptance rates, and Bayesian metrics</p></li> <li><p>The library is pip-installable and comes with Jupyter notebooks examples and documentation. Suggestions, issues, and contributions via GitHub are welcomed.</p></li> </ul> programming tutorial python softwaredevelopment