Argmin Inference over a Discrete Candidate Set
Overview
Provides methods to construct frequentist confidence sets with valid marginal coverage for identifying the population-level argmin or argmax based on IID data. For instance, given an n by p loss matrix—where n is the sample size and p is the number of models—the CS.argmin() method produces a discrete confidence set that contains the model with the minimal (best) expected risk with desired probability. The argmin.HT() method helps check if a specific model should be included in such a confidence set. The main implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) "Winners with confidence: Discrete argmin inference with an application to model selection".
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 15 (2 internal)
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 91%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 40% with a bibliography file
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GitHub Pages site last deployed 2025-07-09
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People & History
- RR 4.6.0 released · 2026-04-24
- 1.1.0Latest2025-07-14 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-07-14
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 54 KB
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