{"library":"scikit-posthocs","title":"scikit-posthocs","description":"Statistical post-hoc analysis and outlier detection algorithms for Python. Provides pairwise multiple comparison tests (e.g., Nemenyi, Conover, Dunn, Bonferroni) and outlier detection (e.g., IQR, Grubbs). Current version: 0.12.0. Release cadence is irregular, major updates every 1-2 years.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install scikit-posthocs"],"cli":null},"imports":["from scikit_posthocs import posthoc_nemenyi_friedman"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom scikit_posthocs import posthoc_nemenyi_friedman\n\ndata = np.array([   [1,2,3],\n                     [2,3,4],\n                     [3,4,5],\n                     [4,5,6] ])\n\nresult = posthoc_nemenyi_friedman(data)\nprint(result)","lang":"python","description":"Perform Nemenyi post-hoc test after Friedman test. Input data: rows = subjects, columns = treatments. Output: pairwise p-value matrix.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}