Open source
Two library stacks, eight packages, installable from PyPI — one for the people who have to evidence an AI system, one for the people who have to measure who a transport network leaves out. Both are libraries rather than hosted products, with no framework lock-in: they compute what the data shows and leave the judgment to you.
Evgraph
For AI governance reviewers and ML engineers
Loose artifacts become one Evidence Graph: a Model Card, an approval, a deployment. A missing approval timestamp stays inconclusive. The scan does not invent the field. EU AI Assurance OS pins evgraph-cli 0.1.2 so the evidence pack and the scan share that gap.
The evgraph package does not install the evgraph command. evgraph-cli does. Findings cite the nodes they came from. A missing timestamp is not a green or red stamp, and the library does not certify regulatory compliance.
- Adapters for Model Card + approval + deployment JSON, dataset manifests, and the MLflow registry
- Reports as JSON, Markdown, SARIF or OSCAL Assessment Results
- A CI promotion gate that is report-only by default; --gate exits non-zero on unmet expectations
- Third-party rule packs register on the evgraph.rules entry point, with no change to core
pip install evgraph evgraph-clievgraph scan model_card.json approval.json deployment.json --format sarifMoveq
For transport researchers, analysts and transport authorities
Turns raw service and demographic data — trips per area, population counts, deprivation ranks — into the standard inequality measures: population-weighted Gini, the Palma ratio, and the Wagstaff concentration index, plus a configurable composite accessibility score.
The core is pure NumPy with no required I/O or GIS dependencies, so it drops into an existing analysis without pulling a stack behind it. For cross-country work, the catalogue registry makes every methodological choice explicit: each measure is declared same, replaced by a national equivalent, or omitted — so omissions are recorded rather than silent.
- Gini, Palma ratio and Wagstaff concentration index, all population-weighted
- Composite scoring that handles missing terms gracefully rather than dropping the row
- A same / replace / omit registry for extending a method to a new country
- CSV-in, numbers-out CLI for quick checks and CI; optional pandas helpers via the frames extra
pip install moveq moveq-climoveq gini areas.csv --population pop --value tripsPackage versions shown here are checked against the PyPI API on every build. If a version drifts, the build fails rather than the page quietly going stale.