Package: lupa 0.1.0

lupa: Data Profiling and Quality Measurement

Provides general-purpose data profiling and quality analysis for tabular data. Discovers structural patterns, implicit types, mixed date formats and reusable findings about potential data issues. Defines quality frameworks with user-declared dimensions and factors, reusable metrics with explicit granularity, typed aggregation, evaluation profiles, versioned histories, structural drift detection, and self-contained HyperText Markup Language reports without external rendering software. Produces editable cleaning plans and applies only explicitly selected actions while retaining an audit log. Includes bounded value distributions, quantiles, mixed-type associations, temporal regularity checks, and confirmable measurement-scale proposals. The general model follows the data-quality lineage described by Batini and Scannapieco (2016) <doi:10.1007/978-3-319-24106-7>. The package ships a traceable implementation of the Data Quality Management Framework for Digital Government, version 1.6, published by the Agency for Electronic Government and the Information and Knowledge Society (AGESIC) (2020) <https://www.gub.uy/agencia-gobierno-electronico-sociedad-informacion-conocimiento/>, and an optional taxonomy of the fifteen characteristics in the International Organization for Standardization and International Electrotechnical Commission standard ISO/IEC 25012:2008 <https://www.iso.org/standard/35736.html>. It also includes the four levels and nineteen principles of the United Nations national quality assurance framework, adapted for Latin America and the Caribbean by the Statistical Conference of the Americas (CEA) of the Economic Commission for Latin America and the Caribbean (CEPAL), based on the United Nations manual (2019) <https://unstats.un.org/unsd/methodology/dataquality/> and the regional guide (2022) <https://repositorio.cepal.org/handle/11362/47464>.

Authors:Sebastián Lucas [aut, cre], Robyn Speer [cph], Nicholas Tierney [cph], Di Cook [cph], Miles McBain [cph], Colin Fay [cph]