Sources and attribution
The initial capability map and methodological safeguards follow Superforecasting Techniques — Skill Pack, reviewed at commit 17afb85d9ad56ba38a1402aa1b6093cceb2fd7ee. The pack is licensed under CC BY 4.0. Supercast implements new Rust routines, typed records, storage, transport, tests, and documentation rather than bundling the pack’s Python helper files.
The original pack’s method and provenance documents distinguish historical research, experimental validation, and workflow adaptations. Consult them for the underlying research attribution. Citing a method does not establish that this software has the same real-world performance as the participants in that research.
For implementation and statistical interpretation:
- SciPy bootstrap documentation describes percentile resampling intervals and contrasts them with other bootstrap methods.
- Stata panel bootstrap guidance explains keeping related observations together during resampling.
- Sebastiano Vigna’s SplitMix64 implementation supplies the public-domain generator adapted for deterministic sampling.
- SQLite transaction documentation describes the concurrency semantics used by the storage adapter.
- mdBook documentation describes the local searchable books and Rust example testing used here.
See the repository’s NOTICE.md and the release bundle’s dependency inventory for additional attribution. No affiliation with or endorsement by the source authors or forecasting organizations is implied.
- Scoring Rules theory documentation gives the equivalent CDF-integral and expectation forms of CRPS used to implement and independently test the routine.