{"id":13504787,"url":"https://github.com/arviz-devs/arviz","last_synced_at":"2025-05-12T07:50:26.745Z","repository":{"id":35616998,"uuid":"39890704","full_name":"arviz-devs/arviz","owner":"arviz-devs","description":"Exploratory analysis of Bayesian models with Python","archived":false,"fork":false,"pushed_at":"2025-04-28T09:37:04.000Z","size":125222,"stargazers_count":1682,"open_issues_count":198,"forks_count":435,"subscribers_count":49,"default_branch":"main","last_synced_at":"2025-05-12T05:42:41.752Z","etag":null,"topics":["bayesian","closember","python"],"latest_commit_sha":null,"homepage":"https://python.arviz.org","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arviz-devs.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":"GOVERNANCE.md","roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"custom":"https://numfocus.org/donate-to-arviz"}},"created_at":"2015-07-29T11:51:10.000Z","updated_at":"2025-05-08T09:36:43.000Z","dependencies_parsed_at":"2023-01-16T01:16:05.139Z","dependency_job_id":"e9f279e9-73f7-4434-9b46-891cde4ac42c","html_url":"https://github.com/arviz-devs/arviz","commit_stats":{"total_commits":1503,"total_committers":172,"mean_commits":8.738372093023257,"dds":0.8263473053892215,"last_synced_commit":"7e51ec9e610a7a271ad5c77e6a21440821429723"},"previous_names":[],"tags_count":39,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arviz-devs%2Farviz","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arviz-devs%2Farviz/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arviz-devs%2Farviz/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arviz-devs%2Farviz/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arviz-devs","download_url":"https://codeload.github.com/arviz-devs/arviz/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253692207,"owners_count":21948312,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian","closember","python"],"created_at":"2024-08-01T00:00:51.419Z","updated_at":"2025-05-12T07:50:26.709Z","avatar_url":"https://github.com/arviz-devs.png","language":"Python","funding_links":["https://numfocus.org/donate-to-arviz"],"categories":["Python","Linear Algebra / Statistics Toolkit","其他_机器学习与深度学习","Uncategorized","模型的可解释性","🎲 Statistics \u0026 Probability","11. Specialized Domains"],"sub_categories":["Statistical Toolkit","Uncategorized","Misc","Tools"],"readme":"\u003cimg src=\"https://raw.githubusercontent.com/arviz-devs/arviz-project/main/arviz_logos/ArviZ.png#gh-light-mode-only\" width=200\u003e\u003c/img\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/arviz-devs/arviz-project/main/arviz_logos/ArviZ_white.png#gh-dark-mode-only\" width=200\u003e\u003c/img\u003e\n\n[![PyPI version](https://badge.fury.io/py/arviz.svg)](https://badge.fury.io/py/arviz)\n[![Azure Build Status](https://dev.azure.com/ArviZ/ArviZ/_apis/build/status/arviz-devs.arviz?branchName=main)](https://dev.azure.com/ArviZ/ArviZ/_build/latest?definitionId=1\u0026branchName=main)\n[![codecov](https://codecov.io/gh/arviz-devs/arviz/branch/main/graph/badge.svg)](https://codecov.io/gh/arviz-devs/arviz)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n[![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/arviz-devs/community)\n[![DOI](http://joss.theoj.org/papers/10.21105/joss.01143/status.svg)](https://doi.org/10.21105/joss.01143) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2540945.svg)](https://doi.org/10.5281/zenodo.2540945)\n[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat\u0026colorA=E1523D\u0026colorB=007D8A)](https://numfocus.org)\n\nArviZ (pronounced \"AR-_vees_\") is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.\n\n### ArviZ in other languages\nArviZ also has a Julia wrapper available [ArviZ.jl](https://julia.arviz.org/).\n\n## Documentation\n\nThe ArviZ documentation can be found in the [official docs](https://python.arviz.org/en/latest/index.html).\nFirst time users may find the [quickstart](https://python.arviz.org/en/latest/getting_started/Introduction.html)\nto be helpful. Additional guidance can be found in the\n[user guide](https://python.arviz.org/en/latest/user_guide/index.html).\n\n\n## Installation\n\n### Stable\nArviZ is available for installation from [PyPI](https://pypi.org/project/arviz/).\nThe latest stable version can be installed using pip:\n\n```\npip install arviz\n```\n\nArviZ is also available through [conda-forge](https://anaconda.org/conda-forge/arviz).\n\n```\nconda install -c conda-forge arviz\n```\n\n### Development\nThe latest development version can be installed from the main branch using pip:\n\n```\npip install git+git://github.com/arviz-devs/arviz.git\n```\n\nAnother option is to clone the repository and install using git and setuptools:\n\n```\ngit clone https://github.com/arviz-devs/arviz.git\ncd arviz\npython setup.py install\n```\n\n-------------------------------------------------------------------------------\n## [Gallery](https://python.arviz.org/en/latest/examples/index.html)\n\n\u003cp\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\n  \u003ctd\u003e\n  \u003ca href= \"https://python.arviz.org/en/latest/examples/plot_forest_ridge.html\"\u003e\n  \u003cimg alt=\"Ridge plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_forest_ridge.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_forest.html\"\u003e\n  \u003cimg alt=\"Forest Plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_forest.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_violin.html\"\u003e\n  \u003cimg alt=\"Violin Plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_violin.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n\u003c/tr\u003e\n\u003ctr\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_ppc.html\"\u003e\n  \u003cimg alt=\"Posterior predictive plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_ppc.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_dot.html\"\u003e\n  \u003cimg alt=\"Joint plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_dot.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_posterior.html\"\u003e\n  \u003cimg alt=\"Posterior plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_posterior.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n\u003c/tr\u003e\n\u003ctr\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_density.html\"\u003e\n  \u003cimg alt=\"Density plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_density.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_pair.html\"\u003e\n  \u003cimg alt=\"Pair plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_pair.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_pair_hex.html\"\u003e\n  \u003cimg alt=\"Hexbin Pair plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_pair_hex.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n\u003c/tr\u003e\n\u003ctr\u003e\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_trace.html\"\u003e\n  \u003cimg alt=\"Trace plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_trace.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_energy.html\"\u003e\n  \u003cimg alt=\"Energy Plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_energy.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n  \u003ctd\u003e\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/plot_rank.html\"\u003e\n  \u003cimg alt=\"Rank Plot\"\n  src=\"https://python.arviz.org/en/latest/_images/mpl_plot_rank.png\" width=\"300\" height=\"auto\" /\u003e\n  \u003c/a\u003e\n  \u003c/td\u003e\n\n\u003c/tr\u003e\n\u003c/table\u003e\n\u003cdiv\u003e\n\n  \u003ca href=\"https://python.arviz.org/en/latest/examples/index.html\"\u003eAnd more...\u003c/a\u003e\n\u003c/div\u003e\n\n## Dependencies\n\nArviZ is tested on Python 3.10, 3.11 and 3.12, and depends on NumPy, SciPy, xarray, and Matplotlib.\n\n\n## Citation\n\n\nIf you use ArviZ and want to cite it please use [![DOI](http://joss.theoj.org/papers/10.21105/joss.01143/status.svg)](https://doi.org/10.21105/joss.01143)\n\nHere is the citation in BibTeX format\n\n```\n@article{arviz_2019,\n  doi = {10.21105/joss.01143},\n  url = {https://doi.org/10.21105/joss.01143},\n  year = {2019},\n  publisher = {The Open Journal},\n  volume = {4},\n  number = {33},\n  pages = {1143},\n  author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin},\n  title = {ArviZ a unified library for exploratory analysis of Bayesian models in Python},\n  journal = {Journal of Open Source Software}\n}\n```\n\n\n## Contributions\nArviZ is a community project and welcomes contributions.\nAdditional information can be found in the [Contributing Readme](https://github.com/arviz-devs/arviz/blob/main/CONTRIBUTING.md)\n\n\n## Code of Conduct\nArviZ wishes to maintain a positive community. Additional details\ncan be found in the [Code of Conduct](https://github.com/arviz-devs/arviz/blob/main/CODE_OF_CONDUCT.md)\n\n## Donations\nArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate [here](https://numfocus.org/donate-to-arviz).\n\n## Sponsors\n[![NumFOCUS](https://www.numfocus.org/wp-content/uploads/2017/07/NumFocus_LRG.png)](https://numfocus.org)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farviz-devs%2Farviz","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farviz-devs%2Farviz","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farviz-devs%2Farviz/lists"}