Data Quality Checks and Statistical Assumption Testing for Agricultural Experiments

Documentation for package ‘agriDQ’ version 0.1.3

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