postcard
1.1.0Estimating Marginal Effects with Prognostic Covariate Adjustment
Overview
Conduct power analyses and inference of marginal effects. Uses plug-in estimation and influence functions to perform robust inference, optionally leveraging historical data to increase precision with prognostic covariate adjustment. The methods are described in Højbjerre-Frandsen et al. (2025) doi:10.48550/arXiv.2503.22284.
Install
Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 1.0 median / 6 max
- Test cases
- 74 / 2.29 per code line
- Documented parameters
- 90%
Test coverage
Line coverage
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Tests / Examples
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Functions
79 15 exported
Complexity
1.7 avg / 6 max
Call network
79 nodes / 74 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.1.0Latest
- 1.0.12025-07-01 · diff ↗
- RR 4.5.0 released · 2025-04-11
- 1.0.02025-04-08
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-04-08
- Total releases
- 3 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 173 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet