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).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.3 |
rolling linux/jammy R-4.5 | agriDQ_0.1.3.tar.gz |
141.6 KiB |
0.1.3 |
rolling linux/noble R-4.5 | agriDQ_0.1.3.tar.gz |
141.2 KiB |
0.1.3 |
rolling source/ R- | agriDQ_0.1.3.tar.gz |
38.0 KiB |
0.1.3 |
latest linux/jammy R-4.5 | agriDQ_0.1.3.tar.gz |
141.6 KiB |
0.1.3 |
latest linux/noble R-4.5 | agriDQ_0.1.3.tar.gz |
141.2 KiB |
0.1.3 |
latest source/ R- | agriDQ_0.1.3.tar.gz |
38.0 KiB |
0.1.3 |
2026-04-26 source/ R- | agriDQ_0.1.3.tar.gz |
38.0 KiB |
0.1.3 |
2026-04-23 source/ R- | agriDQ_0.1.3.tar.gz |
38.0 KiB |