{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Provides JAX-compatible implementations of elliptic integrals (ellipk, ellipkm1, ellipe) that are differentiable and JIT-compilable, addressing the gap in jax.scipy.special.","skillfed_tags":["jax-compatible","autodiff","special-functions"],"use_cases":["Compute elliptic integrals inside JAX-compiled functions (jit, vmap, grad) without leaving the JAX ecosystem.","Differentiate functions that depend on elliptic integrals using jax.grad, jax.jacobian, or jax.hessian.","Replace scipy.special calls in numerical code when JIT compilation or autodiff is needed.","Implement physics or engineering models (e.g., pendulum dynamics, elliptic orbits) with automatic differentiation.","Build machine learning models that incorporate elliptic integrals as part of a larger differentiable computation graph."],"what_it_does":"jaxellip fills a gap in JAX's scientific computing stack by providing differentiable, JIT-compatible implementations of three complete elliptic integrals: ellipk, ellipkm1, and ellipe. These functions are available in scipy.special but not in jax.scipy.special, making them incompatible with JAX's autodiff and compilation. The package uses Cephes minimax polynomials (the same method as SciPy) for forward evaluation and custom JVP rules for derivatives, enabling full differentiability in forward mode, reverse mode, and higher-order derivatives like jax.hessian.\n\nThe implementation handles numerical edge cases consistently with SciPy\u2014ellipk(1) returns inf, ellipe(1) returns 1, and out-of-domain inputs return NaN. Results are tested against arbitrary-precision mpmath references and validated against scipy.special to about 1e-14 relative accuracy. The package requires only jax as a runtime dependency and supports Python 3.12\u20133.14.","worth_installing":"Yes, if you need elliptic integrals inside JAX-compiled or autodiff code. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is MIT-licensed. The 0.1.0 release is recent and tested against mpmath and scipy.special. Install only if you actually use elliptic integrals; otherwise it adds an unnecessary dependency."},"id":"jaxellip","links":{"html":"https://skillfed.io/packages/jaxellip","md":"https://skillfed.io/packages/jaxellip.md","pypi":"https://pypi.org/project/jaxellip/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-01","license_spdx":"MIT","license_treatment":"permissive","name":"jaxellip","python_support":"supports_current","summary":"Some elliptic integrals in JAX"},"popularity":{"monthly_downloads":101503,"position":12928,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.0"}