The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
agriDQ provides a comprehensive pipeline for data quality checks and statistical assumption diagnostics in agricultural experimental data.
# From CRAN
install.packages("agriDQ")| Category | Functions |
|---|---|
| Outlier detection | IQR fence, Z-score, modified Z-score (Hampel), Grubbs, Dixon Q-test |
| Missing data | Little’s MCAR test, MAR/MNAR pattern analysis |
| Normality | Shapiro-Wilk, Anderson-Darling, Lilliefors, Jarque-Bera, KS, Pearson |
| Homogeneity | Bartlett, Levene, Fligner-Killeen |
| Independence | Durbin-Watson, Breusch-Godfrey, Wald-Wolfowitz runs test |
| Design check | CRD, RCBD, LSD, factorial design validation |
| Qualitative | Consistency checks for categorical variables |
| Report | Automated HTML report generation |
library(agriDQ)
# Load example data
data(agri_trial)
# Full pipeline
result <- run_agriDQ_pipeline(agri_trial,
response = "yield",
treatment = "treatment",
block = "block")
print(result)
# HTML report
generate_agriDQ_report(result, output_file = "report.html")Gomez, K.A. and Gomez, A.A. (1984). Statistical Procedures for Agricultural Research, 2nd ed. Wiley. ISBN: 978-0471870920.
Montgomery, D.C. (2017). Design and Analysis of Experiments, 9th ed. Wiley. ISBN: 978-1119492443.
GPL (>= 3)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.