argminCS: Argmin Inference over a Discrete Candidate Set
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".
| Version: | 1.1.0 |
| Imports: | BSDA,glue,LDATS,MASS, methods,Rdpack, stats,withr |
| Published: | 2025-07-14 |
| DOI: | 10.32614/CRAN.package.argminCS |
| Author: | Tianyu Zhang [aut], Hao Lee [aut, cre, cph], Jing Lei [aut] |
| Maintainer: | Hao Lee <haolee at andrew.cmu.edu> |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/xu3cl4/argminCS |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | argminCS results |
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