# agriDQ

**agriDQ** provides a comprehensive pipeline for data quality checks and
statistical assumption diagnostics in agricultural experimental data.

## Installation

```r
# From CRAN
install.packages("agriDQ")
```

## Functions

| 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 |

## Quick start

```r
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")
```

## References

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.

## License

GPL (>= 3)
