Calculate a directly age-standardized rate with Fay-Feuer gamma confidence bounds

baltor_direct_age_standardization

Calculate a directly age-standardized rate with Fay-Feuer gamma confidence bounds from age-specific event counts, person-years and a caller-supplied standard population, refusing gaps, overlaps and mismatched age groups.

Files, names only

LICENSE 1.0 KB
SKILL.md 5.4 KB
contracts/input.schema.json 2.5 KB
contracts/output.schema.json 6.1 KB
examples/example.expected.json 1.4 KB
examples/example.input.json 749 bytes
references/provenance.json 5.2 KB
scripts/tool.py 11.5 KB
verification/test_tool.py 12.8 KB

Full files in your harness.

What it takes and gives

Command: python3 -B scripts/tool.py < examples/example.input.json
Input fields: rate_multiplier, confidence_level, age_groups
Test: python3 -B -m unittest discover -s verification -p 'test_*.py' -v
Files: SKILL.md, contracts/input.schema.json, contracts/output.schema.json, scripts/tool.py
Placed for: OpenCode

Limits and scope

Limits: This is a supporting calculation for SDG target 3.9 and other health rates on caller-supplied counts, not an official age-standardized estimate. A standardized rate is only comparable with rates standardized to the same standard population and the same age bands.

Details

Kind: Skill
Licence: MIT
Declared effects: reads files, runs commands
Placed for: OpenCode
Source: docs/architecture/NORTH-STAR.md
Size: 9 files, 46.7 KB
Identity: baltor_direct_age_standardization
Version: ab45ad761bb598d4c4742fd5946990236d77b4667931de820c4e1e469d105671

The version is the SHA-256 of the item your harness downloads; every file in it names its own checksum.

Where it is shown

Calculators for public statisticsDeterministic skills

Related components

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