{"id":48082587,"url":"https://github.com/sandialabs/conin","last_synced_at":"2026-04-04T14:58:14.438Z","repository":{"id":297261688,"uuid":"994766568","full_name":"sandialabs/conin","owner":"sandialabs","description":"A python library that supports the constrained analysis of probabilistic graphical models","archived":false,"fork":false,"pushed_at":"2026-03-15T11:34:07.000Z","size":3297,"stargazers_count":4,"open_issues_count":16,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-16T00:10:15.948Z","etag":null,"topics":["scr-3226","snl-applications","snl-data-analysis","snl-science-libs"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sandialabs.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-02T12:57:11.000Z","updated_at":"2026-03-15T11:34:56.000Z","dependencies_parsed_at":"2025-12-17T02:07:03.182Z","dependency_job_id":null,"html_url":"https://github.com/sandialabs/conin","commit_stats":null,"previous_names":["sandialabs/conin"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/sandialabs/conin","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fconin","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fconin/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fconin/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fconin/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandialabs","download_url":"https://codeload.github.com/sandialabs/conin/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2Fconin/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31403947,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-04T10:20:44.708Z","status":"ssl_error","status_checked_at":"2026-04-04T10:20:06.846Z","response_time":60,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["scr-3226","snl-applications","snl-data-analysis","snl-science-libs"],"created_at":"2026-04-04T14:58:13.781Z","updated_at":"2026-04-04T14:58:14.429Z","avatar_url":"https://github.com/sandialabs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg src=\"doc/logos/conin_logo_large.png\" align=\"center\" alt=\"CONIN Logo\" width=\"450\"/\u003e\nA Python library that supports the constrained analysis of probabilistic graphical models \n\n--------------------------------------------------------------------------------\n\n[![Pytest Tests](https://github.com/sandialabs/conin/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/sandialabs/conin/actions/workflows/pytest.yml?query=branch%3Amain)\n[![codecov](https://codecov.io/gh/sandialabs/conin/branch/main/graph/badge.svg)](https://codecov.io/gh/sandialabs/conin)\n[![Documentation Status](https://readthedocs.org/projects/conin/badge/?version=latest)](http://conin.readthedocs.org/en/latest/)\n[![GitHub contributors](https://img.shields.io/github/contributors/sandialabs/conin.svg)](https://github.com/sandialabs/conin/graphs/contributors)\n[![Merged PRs](https://img.shields.io/github/issues-pr-closed-raw/sandialabs/conin.svg?label=merged+PRs)](https://github.com/sandialabs/conin/pulls?q=is:pr+is:merged)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\n## Overview\n\nConin supports constrained inference and learning for hidden Markov models, Bayesian networks, dynamic Bayesian networks and Markov networks. Conin interfaces with the pgmpy python library for the specification of general probabilistic graphical models. Additionally, it interfaces with a variety of optimization solvers to support learning and inference.\n\n## Testing\n\nConin tests can be executed using pytest:\n\n```\ncd conin\npytest .\n```\n\nIf the pytest-cov package is installed, pytest can provide coverage statistics:\n\n```\ncd conin\npytest --cov=conin .\n```\n\nThe following options list the lines that are missing from coverage tests:\n```\ncd conin\npytest --cov=conin --cov-report term-missing .\n```\n\nNote that pytest coverage includes coverage of test files themselves.  This gives a somewhat skewed sense of coverage for the code base, but it helps identify tests that are omitted or not executed completely.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fconin","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Fconin","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fconin/lists"}