Audit Statistical Fidelity of AI-Mediated Official Statistics R package AI4OfficialStats version 0.1.0 Provides deterministic tools for auditing whether artificial intelligence systems preserve the numerical, semantic, contextual, temporal, geographic, unit, provenance, revision, transformation, and uncertainty properties of official statistics. Structured reference statistics and machine-generated claims can be compared using non-compensatory critical-error rules, weakest-link and geometric fidelity summaries, provenance graphs, and portable SHA-256 proof bundles. The package provides bounded connectors for official statistical services and an extensible HTTPS JSON API registry. Prompt perturbation, statistical red-team generation, minimal-pair tests, and benchmark data support reproducible evaluation of generative, retrieval-augmented, and agentic statistical systems. An embedded alignment layer maps claim-level controls to relevant activities of the Generic Statistical Business Process Model (GSBPM) 5.2, including Analyse, Disseminate, Evaluate, Quality Management, and Metadata Management. The GSBPM alignment follows United Nations Economic Commission for Europe (2025) "Generic Statistical Business Process Model (GSBPM) version 5.2" <https://unece.org/statistics/gsbpm-v5.2>. Software Depends: R (>= 4.1.0) Imports: digest, jsonlite, stats, utils Suggests: testthat (>= 3.0.0) Leila Marvian Mashhad <leila.marveian@gmail.com> Comprehensive R Archive Network (CRAN) Hossein Hassani [aut], Steve MacFeely [aut], Leila Marvian Mashhad [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=AI4OfficialStats/LICENSE) 2026-09-22 application/tgz https://CRAN.R-project.org/package=AI4OfficialStats doi:10.32614/CRAN.package.AI4OfficialStats