R: Data Quality Checks and Statistical Assumption Testing for Agricultural Experiments

# Data Quality Checks and Statistical Assumption Testing for Agricultural Experiments

## Documentation for package ‘agriDQ’ version 0.1.3

- DESCRIPTION file.
- Package NEWS.

## Help Pages

agri_trial | Simulated wheat variety trial dataset (RCBD)
check_design | Validate experimental design structure and balance
check_homogeneity | Test homogeneity of variance across treatment groups
check_independence | Test independence of residuals / errors
check_missing | Analyse missing data patterns and classify missingness mechanism
check_normality | Comprehensive normality testing for agricultural experimental data
check_outliers | Univariate outlier detection for agricultural experimental data
check_outliers_mv | Multivariate outlier detection using Mahalanobis distance
check_qualitative | Check quality of categorical / qualitative variables
classify_missing | Classify missingness mechanism per variable using logistic regression
generate_dq_report | Generate an automated HTML data quality report
print.agriDQ_result | Print an agriDQ_result object
run_dq_pipeline | Run the complete data quality pipeline
standardise_labels | Standardise categorical labels in a data frame