{"id":16390698,"url":"https://github.com/mkazmier/best-test","last_synced_at":"2026-06-09T18:30:15.298Z","repository":{"id":106514803,"uuid":"105641222","full_name":"mkazmier/best-test","owner":"mkazmier","description":"BEST test - Bayesian difference test inspired by the classic BEST paper","archived":false,"fork":false,"pushed_at":"2017-10-03T11:00:29.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-03T14:47:48.048Z","etag":null,"topics":["bayesian-inference","bayesian-statistics","statistical-tests"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mkazmier.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2017-10-03T10:53:45.000Z","updated_at":"2017-10-03T10:59:15.000Z","dependencies_parsed_at":"2023-07-19T04:16:14.171Z","dependency_job_id":null,"html_url":"https://github.com/mkazmier/best-test","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkazmier%2Fbest-test","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkazmier%2Fbest-test/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkazmier%2Fbest-test/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkazmier%2Fbest-test/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mkazmier","download_url":"https://codeload.github.com/mkazmier/best-test/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240221348,"owners_count":19767442,"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-inference","bayesian-statistics","statistical-tests"],"created_at":"2024-10-11T04:44:00.501Z","updated_at":"2026-06-09T18:30:15.201Z","avatar_url":"https://github.com/mkazmier.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# BEST test\n\n## Description\nPerform the Bayesian test for difference of means and standard deviations between parameters as described in the classic [BEST paper](http://www.indiana.edu/~kruschke/articles/Kruschke2013JEPG.pdf) (Kruschke, 2012). The prior distributions used are the same as in the paper (Student's T for data, normal for means, uniform for standard deviations, shifted exponential for normality)\n\n## Example usage\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom best_test import BayesianDifferenceTest\n\nobserved_a = np.random.random(100)\nobserved_b = np.random.random(85) # unequal sample sizes are not an issue\n\nmu_mean = 0 # mean for the prior on mean\nmu_sd = 1 # standard deviation on the prior on mean\nsd_lower = .1 # lower bound for the prior on standard deviation\nsd_upper = 10 # upper bound for the prior on standard deviation\nnu_mean = 30 # mean on the prior on nu (normality, aka 'degrees of freedom')\n\ntest = BayesianDifferenceTest('param_a_name', 'param_b_name',\n                               mu_mean, mu_sd, \n                               sd_lower, sd_upper,\n                               nu_mean)\ntest.run(observed_a, observed_b)\ntest.plot_posterior(ref_val=0)\nplt.show()\n```\n\n## Requirements\n- Python \u003e= 3.4\n- pymc3 \u003e= 3.1\n\n## Notes\nThis program was written as a part of my internship in MAASTRO Clinic Knowledge Engineering team. It is available under the MIT licence.\n\n## References\n- Bayesian estimation supersedes the t test. Kruschke JK. J Exp Psychol Gen. 2013 May;142(2):573-603. doi: 10.1037/a0029146. Epub 2012 Jul 9.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkazmier%2Fbest-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmkazmier%2Fbest-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkazmier%2Fbest-test/lists"}