{ "_id": "6a16a4e9acfb0bcc41d8215a", "Type": "Package", "Package": "missalpha", "Title": "Find Range of Cronbach Alpha with a Dataset Including Missing\nData", "Version": "0.2.0", "Authors@R": "c(\nperson(\"Feng\", \"Ji\", email = \"f.ji@utoronto.ca\", role = c(\"aut\")),\nperson(\"Biying\", \"Zhou\", email = \"biying.zhou@psu.edu\", role = c(\"aut\",\"cre\")))", "Maintainer": "Biying Zhou ", "Description": "Provides functions to calculate the minimum and maximum\npossible values of Cronbach's alpha when item-level missing\ndata are present. Cronbach's alpha (Cronbach, 1951\n) is one of the most widely used\nmeasures of internal consistency in the social, behavioral, and\nmedical sciences (Bland & Altman, 1997\n; Tavakol & Dennick, 2011\n). However, conventional\nimplementations assume complete data, and listwise deletion is\noften applied when missingness occurs, which can lead to biased\nor overly optimistic reliability estimates (Enders, 2003\n). This package implements\ncomputational strategies including enumeration, Monte Carlo\nsampling, and optimization algorithms (e.g., Genetic Algorithm,\nDifferential Evolution, Sequential Least Squares Programming)\nto obtain sharp lower and upper bounds of Cronbach's alpha\nunder arbitrary missing data patterns. The approach is\nmotivated by Manski's partial identification framework and\npessimistic bounding ideas from optimization literature.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-27 07:57:21 UTC", "User": "root" }, "Author": "Feng Ji [aut], Biying Zhou [aut, cre]", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-10-29 15:39:48 UTC", "RemoteUrl": "https://github.com/cran/missalpha", "RemoteRef": "HEAD", "RemoteSha": "02d76680e8ff0b0ce98326dc3aee39013308fb58", "MD5sum": "af49787870bcce5c177838dc13a6f7df", "_user": "cran", "_type": "src", "_file": "missalpha_0.2.0.tar.gz", "_fileid": "a176426e61ac63e846677ce4fbe3c406d5b4a45a31e7476b2e9cde0a26b473f4", "_filesize": 150233, "_sha256": "a176426e61ac63e846677ce4fbe3c406d5b4a45a31e7476b2e9cde0a26b473f4", "_created": "2026-05-27T07:57:21.000Z", "_published": 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