Package: AI4OfficialStats
Type: Package
Title: Audit Statistical Fidelity of AI-Mediated Official Statistics
Version: 0.2.0
Authors@R: c(
    person("Hossein", "Hassani", role = "aut"),
    person("Steve", "MacFeely", role = "aut"),
    person("Leila", "Marvian Mashhad",
           email = "leila.marveian@gmail.com",
           role = c("aut", "cre"))
    )
Description: 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, an
    easy schema-detection and file-import layer for arbitrary official
    organisations, extensible provider registries, and a search-first natural-
    language verification layer that classifies statistical claims, selects
    suitable official sources, retrieves candidate evidence, matches statistical
    dimensions, and compares claimed values. If no reference year is stated,
    verification uses the latest available matching official observation and
    discloses the resolved year. Source attribution is optional:
    automatic routing can choose suitable providers when none is named, while
    explicitly named supported sources are respected by default. Automatic
    catalogue-to-observation verification is implemented for the World Bank,
    WHO, the United Nations Statistics Division Sustainable Development Goals
    service, and the European Commission statistical service,
    while other providers remain available through bounded direct connectors or
    generic official-data import. 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.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: digest, jsonlite, stats, utils
Suggests: testthat (>= 3.0.0), readxl
Config/testthat/edition: 3
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-09-23 18:52:48 UTC; marvian
Author: Hossein Hassani [aut],
  Steve MacFeely [aut],
  Leila Marvian Mashhad [aut, cre]
Maintainer: Leila Marvian Mashhad <leila.marveian@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-24 09:10:22 UTC
