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argminCS

Argmin Inference over a Discrete Candidate Set

v1.1.0 · Jul 14, 2025 · MIT + file LICENSE

Description

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".

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Dependency Network

Dependencies Reverse dependencies BSDA glue LDATS MASS Rdpack withr argminCS

Version History

1 tracked
new 1.1.0 Mar 10, 2026

R Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Jul 14, 2025. Releases before tracking aren’t shown.