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agriDQ
Data Quality Checks and Statistical Assumption Testing for Agricultural Experiments
View on CRAN: Click
here
Download and install agriDQ package within the R console
Install from CRAN:
install.packages("agriDQ")
Install from Github:
library("remotes")
install_github("cran/agriDQ") Install by package version:
library("remotes")
install_version("agriDQ", "0.1.3") Attach the package and use:
library("agriDQ")
Maintained by
Sadikul Islam
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
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First Published: 2026-04-21
Latest Update: 2026-04-21
Description:
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).
How to cite:
Sadikul Islam (2026). agriDQ: Data Quality Checks and Statistical Assumption Testing for Agricultural Experiments. R package version 0.1.3, https://cran.r-project.org/web/packages/agriDQ. Accessed 24 Oct. 2026.
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