Comprehensive Data Quality Auditing and Validation // Provides tools for systematic assessment, validation, and reporting of data quality. The package detects common data-quality problems including missing or blank values, duplicate observations, infinite values, constant and near-zero variance variables, outliers, invalid ranges and categories, type and pattern violations, problematic dates and identifiers, sequence errors, dependency violations, and cross-variable inconsistencies. It also supports reusable validation rules, integrated audit reports, and standardized data-quality scoring for reproducible data-quality assessment workflows.