{ "_id": "6a0f65c5acfb0bcc41c5d298", "Package": "agriDQ", "Title": "Data Quality Checks and Statistical Assumption Testing for\nAgricultural Experiments", "Version": "0.1.3", "Authors@R": "person(\ngiven = \"Sadikul\",\nfamily = \"Islam\",\nemail = \"sadikul.islamiasri@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2924-7122\")\n)", "Description": "Provides a comprehensive pipeline for data quality checks\nand statistical assumption diagnostics in agricultural\nexperimental data. Functions cover outlier detection using\nInterquartile Range (IQR) fence, Z-score, modified Z-score\n(Hampel identifier), Grubbs test and Dixon Q-test with\nconsensus flagging; missing data pattern analysis and mechanism\nclassification (Missing Completely At Random/Missing At\nRandom/Missing Not At Random (MCAR/MAR/MNAR)) via Little's\ntest; normality testing using Shapiro-Wilk, Anderson-Darling,\nKolmogorov-Smirnov, Lilliefors, Pearson chi-square and\nJarque-Bera tests; homogeneity of variance via Bartlett, Levene\nand Fligner-Killeen tests; independence of errors via\nDurbin-Watson, Breusch-Godfrey and Wald-Wolfowitz runs tests;\nexperimental design validation for Completely Randomised Design\n(CRD), Randomised Complete Block Design (RCBD), Latin Square\nDesign (LSD) and factorial designs; qualitative variable\nconsistency checks; and automated HyperText Markup Language\n(HTML) report generation. Designed to align with Findable,\nAccessible, Interoperable and Reusable (FAIR) data principles.\nMethods follow Gomez and Gomez (1984, ISBN:978-0471870920) and\nMontgomery (2017, ISBN:978-1119492443).", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-21 09:58:04 UTC", "User": "root" }, "Author": "Sadikul Islam [aut, cre] (ORCID:\n)", "Maintainer": "Sadikul Islam ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-04-21 19:42:01 UTC", "RemoteUrl": "https://github.com/cran/agriDQ", "RemoteRef": "HEAD", "RemoteSha": "eb751ad5c66b0c82091fbd929c56647af37e0290", "MD5sum": "b5c54fd032a277ec95a970a6f4c0d176", "_user": "cran", "_type": "src", "_file": "agriDQ_0.1.3.tar.gz", "_fileid": "112f6803d8faf91af7a4ae21130288f1fbf321d57c1b138a11c37b962b6578a1", "_filesize": 224275, "_sha256": 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"fields": [ "plot_id", "block", "treatment", "variety", "yield", "plant_height", "tillers" ], "rows": 20, "table": true, "tojson": true } ], "_help": [ { "page": "agri_trial", "title": "Simulated wheat variety trial dataset (RCBD)", "topics": [ "agri_trial" ] }, { "page": "check_design", "title": "Validate experimental design structure and balance", "topics": [ "check_design" ] }, { "page": "check_homogeneity", "title": "Test homogeneity of variance across treatment groups", "topics": [ "check_homogeneity" ] }, { "page": "check_independence", "title": "Test independence of residuals / errors", "topics": [ "check_independence" ] }, { "page": "check_missing", "title": "Analyse missing data patterns and classify missingness mechanism", "topics": [ "check_missing" ] }, { "page": "check_normality", "title": "Comprehensive normality testing for agricultural experimental data", "topics": [ "check_normality" ] }, { "page": "check_outliers", "title": "Univariate outlier detection for 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