{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"nutpie provides a fast NUTS sampler for Bayesian inference on PyMC and Stan models, using a Rust-based implementation.","skillfed_tags":["bayesian-inference","mcmc-sampling","rust-accelerated"],"use_cases":["Accelerate sampling for hierarchical models by replacing default samplers without rewriting model code.","Sample large models where gradient evaluation dominates runtime, using Rust backend for speed.","Interactively monitor and control long-running Bayesian inference via pause/resume without blocking.","Combine with arviz for post-sampling diagnostics and visualization of posterior traces.","Prototype models, then accelerate sampling without changing model definitions."],"what_it_does":"nutpie is a Bayesian sampling library that wraps nuts-rs, a Rust implementation of the No-U-Turn Sampler algorithm. It compiles probabilistic models and samples from their posterior distributions, returning results as ArviZ InferenceData objects compatible with standard Bayesian workflows. The package targets users who want faster sampling, particularly for hierarchical and complex models where gradient evaluation is a bottleneck.\n\nThe library integrates with existing model ecosystems, accepting model definitions in native syntax and producing compatible output. It supports both blocking and non-blocking sampling modes, allowing interactive control (pause, resume, abort) over long-running inference tasks. Dependencies include data handling (pandas, pyarrow, xarray), statistical output (arviz), and storage (zarr, obstore), reflecting its role as a sampler within existing Bayesian analysis pipelines.","worth_installing":"Yes, if you use Bayesian modeling and want faster sampling with minimal code changes. The package is actively maintained with recent releases, has no known vulnerabilities, and offers pre-built wheels for modern Python versions. Install friction is moderate due to Rust compilation on source builds, but pre-built wheels mitigate this for standard platforms. The MIT license poses no restrictions. Verify that sampling efficiency gains apply to your specific models before adopting as a primary sampler."},"id":"nutpie","links":{"html":"https://skillfed.io/packages/nutpie","md":"https://skillfed.io/packages/nutpie.md","pypi":"https://pypi.org/project/nutpie/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-30","license_spdx":null,"license_treatment":"permissive","name":"nutpie","python_support":"supports_current","summary":"Sample Stan or PyMC models"},"popularity":{"monthly_downloads":305005,"position":7800,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.16.11"}