{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"arviz-plots provides visualization functions for Bayesian model analysis, handling posterior diagnostics, model comparison, and workflow visualization through composable plot components.","skillfed_tags":["bayesian-inference","visualization","mcmc-diagnostics"],"use_cases":["Visualize posterior distributions and trace plots for MCMC diagnostics and convergence assessment.","Compare multiple Bayesian models using side-by-side or comparative plotting functions.","Generate posterior predictive check plots to validate model fit against observed data.","Create publication-ready diagnostic plots for Bayesian model reports and papers.","Explore high-dimensional posterior samples interactively using bokeh or plotly backends."],"what_it_does":"arviz-plots is the visualization subpackage of ArviZ, a Python library for exploratory analysis of Bayesian models. It provides ready-to-use, composable plotting functions for posterior analysis, model checking, comparison, and diagnostics. The package is intentionally minimal\u2014it depends only on xarray, numpy, arviz-base, and arviz-stats\u2014and does not bundle any plotting backend by default. You choose which backend to install (matplotlib, bokeh, or plotly) based on your needs, keeping the core package lightweight.\n\nThe package is designed for Bayesian practitioners at all levels, from first-time modelers to experienced researchers. It integrates with the broader ArviZ ecosystem and supports modern Python versions (3.12+). With active maintenance, recent releases, and permissive licensing, it is positioned as a stable, community-driven tool for Bayesian workflow visualization.","worth_installing":"Yes. arviz-plots is actively maintained, has zero known vulnerabilities, supports current Python versions, and carries permissive licensing. Install it if you are doing Bayesian modeling with ArviZ and need visualization\u2014but remember to also install your chosen backend (e.g., `pip install arviz-plots[matplotlib]`) to actually render plots."},"id":"arviz-plots","links":{"html":"https://skillfed.io/packages/arviz-plots","md":"https://skillfed.io/packages/arviz-plots.md","pypi":"https://pypi.org/project/arviz-plots/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"arviz-plots","python_support":"supports_current","summary":"ArviZ-plots provides ready to use and composable plots for Bayesian Workflow."},"popularity":{"monthly_downloads":599765,"position":5824,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}