agriDQ: Data Quality Checks and Statistical Assumption Testing for
Agricultural Experiments
Provides a comprehensive pipeline for data quality checks and
statistical assumption diagnostics in agricultural experimental data.
Functions cover outlier detection using Interquartile Range (IQR) fence,
Z-score, modified Z-score (Hampel identifier), Grubbs test and Dixon
Q-test with consensus flagging; missing data pattern analysis and
mechanism classification (Missing Completely At Random/Missing At
Random/Missing Not At Random (MCAR/MAR/MNAR)) via Little's test;
normality testing using Shapiro-Wilk, Anderson-Darling,
Kolmogorov-Smirnov, Lilliefors, Pearson chi-square and Jarque-Bera tests;
homogeneity of variance via Bartlett, Levene and Fligner-Killeen tests;
independence of errors via Durbin-Watson, Breusch-Godfrey and
Wald-Wolfowitz runs tests; experimental design validation for Completely
Randomised Design (CRD), Randomised Complete Block Design (RCBD), Latin
Square Design (LSD) and factorial designs; qualitative variable consistency
checks; and automated HyperText Markup Language (HTML) report generation.
Designed to align with Findable, Accessible, Interoperable and Reusable
(FAIR) data principles. Methods follow Gomez and Gomez (1984,
ISBN:978-0471870920) and Montgomery (2017, ISBN:978-1119492443).
| Version: |
0.1.3 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats, graphics, grDevices, utils, nortest, car, lmtest, tseries, stringdist |
| Suggests: |
testthat (≥ 3.0.0), covr, MASS |
| Published: |
2026-04-21 |
| DOI: |
10.32614/CRAN.package.agriDQ (may not be active yet) |
| Author: |
Sadikul Islam 
[aut, cre] |
| Maintainer: |
Sadikul Islam <sadikul.islamiasri at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
agriDQ results |
Documentation:
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