Anim

Anim is local research software that checks whether an event-based model (EBM) result changes when you run predeclared, scientifically reasonable alternatives. You connect your EBM as a local worker. Anim does not import, select, download, certify, or silently replace your model.

It is not a diagnostic, prognostic, treatment, causal, regulatory, or medical-device tool. An emitted event order is not evidence that a recoverable disease-order signal exists.

Installation

Anim 0.1.1 requires CPython 3.12:

python3.12 -m pip install anim
ebm-audit doctor

Anim 0.1.1 installs and runs doctor on macOS and Linux. Worker execution uses the reviewed Seatbelt path on macOS. On Linux, it uses /usr/bin/bwrap when Bubblewrap is installed and otherwise fails closed with PRIVACY.CONTAINMENT_UNAVAILABLE. The 0.1.1 release does not claim full Linux worker-execution support.

For development from a source checkout:

python3.12 --version
uv sync --frozen
uv run ebm-audit doctor

Smallest Runnable Example

Run the project-owned synthetic demo before opening or connecting any participant data:

ebm-audit demo --conformance-ebm

It runs offline and uses only synthetic project-owned data. Its expected bounded outcome is PARTIAL with an INCOMPLETE report at ebm-audit-demo/report/. That result exercises the worker and report paths. It is not scientific validation and does not accept a backend.

For a transferred offline kit, follow the offline kit guide. Its installed command can be run from the supplied virtual environment:

cd "$PROOF_ROOT" || exit 1
"$VENV_ROOT/bin/ebm-audit" demo --conformance-ebm

1. Open ebm-audit-demo/report/report.html locally and inspect the matching ebm-audit-demo/report/report.json, ebm-audit-demo/report/universes.csv, and ebm-audit-demo/report/warnings.jsonl files. Inspect warnings.jsonl for visible cautions and diagnostics.
2. Read How the synthetic route works before changing a worker. It follows a deliberately tiny two-event synthetic example from a request shape to the visible audit artifacts.
3. To start a researcher-owned worker project, run ebm-audit adapter init /approved/local-config/my-ebm-worker, then follow the generated README.md. The generated backend is a SYNTHETIC-ONLY transport example, not an EBM.
4. If the model lives in a private Jupyter notebook, follow the frozen notebook handoff. The notebook and data remain private and local.

What The Audit Checks

For evidence that a worker can actually supply, the auditor keeps these questions separate:

• How much order or stage uncertainty exists within one fit.
• How much results change across independent chains or seeds.
• How sampling, declared analyst decisions, and participant removal change it.
• Whether synthetic no-signal controls show an apparent result when they should not.
• Whether a worker omitted a capability, failed, or returned invalid evidence.

An emitted order is not proof that a disease-order signal is recoverable. The auditor makes sensitivity and missing evidence visible; it does not diagnose, predict, recommend treatment, or establish a causal result.

Read Next

Do not copy participant rows, private column names, raw values, reversible mappings, or local paths into this repository, reports, tickets, chat, or a corpus note. A real-data integration is optional downstream work that needs its own local permission, privacy review, scientific review, and worker evidence.

The supported public integration surface is the ebm-audit CLI and the Python package ebm_audit.worker_sdk. cli_workflows and reporting modules are auditor internals, not alternate worker-integration APIs.

Read the result states

• Warnings are visible cautions or diagnostics, not automatic failure or permission to ignore a scientific gate.
• UNSUPPORTED_CAPABILITY means the worker cannot perform a requested output. It is an explicit non-success universe, not missing evidence and not a pass.
• UNAVAILABLE in the training-stage status fields means required evidence cannot be supplied. It remains visible. It is neither pass nor fail.
• NOT_APPLICABLE in the training-stage status fields means evidence is outside the declared capability or analysis scope. It remains visible. It is neither pass nor fail.
• Inspect capability_evidence.training_stage.posterior.status, capability_evidence.training_stage.hard_stage.status, and capability_evidence.training_stage.expected_stage.status for the declared training-stage capability state.
• Failed universes remain visible with their terminal final_status in candidate_records[].final_status and ebm-audit-demo/report/universes.csv. Do not silently drop them or interpret them as successful scientific evidence.

What this PARTIAL/INCOMPLETE result can establish

A PARTIAL/INCOMPLETE audit supports only the visible protocol and capability-limited synthetic evidence actually present in its report. It does not establish a recoverable disease-order signal, scientific validity, diagnosis, prognosis, treatment, or causal claims. Unavailable evidence remains missing and is neither pass nor fail. Product readiness and worker integration do not certify pysaebm, PySuStaIn, or any named or future EBM backend.

Status And Authority

The only product-readiness state is:

READY FOR RESEARCHERS TO INTEGRATE AN EBM AND RUN THE AUDITOR LOCALLY

The backend-neutral integration and local audit path has completed the project's synthetic readiness review. This is software readiness, not scientific approval of an EBM or dataset.

It means a researcher can connect a local EBM worker and run the auditor without the original developer's help. It does not accept a named backend, validate an untested integration, establish a disease-order signal, or authorize participant-data use. The exact claim boundary is the normative EBM integration readiness contract.

See the 0.1.1 changelog for the public release scope.

Anim is licensed under the Apache License 2.0.