smsncut: Optimal Diagnostic Cutoff Selection under Scale Mixtures of
Skew-Normal Distributions
Implements a parametric decision-theoretic framework for optimal
diagnostic cutoff selection under the family of scale mixtures of skew-normal
(SMSN) distributions, including the skew-normal (SN) and skew-t (ST) models
as special cases. The optimal cutoff is defined by minimising a weighted
misclassification risk that incorporates disease prevalence and asymmetric
costs, leading to a likelihood-ratio equation that generalises the Youden
criterion. Under a monotone likelihood ratio condition, existence, uniqueness,
and global optimality of the cutoff are established. Asymptotic normality and
a closed-form plug-in variance estimator are provided via the implicit function
theorem and the multivariate delta method. Tools for model fitting, cutoff
estimation, confidence intervals, the local identifiability diagnostic, and
Monte Carlo simulation are included. The methodology is described in
de Paula, Mouriño, and Dias Domingues (2026)
<doi:10.48550/arXiv.2605.07829>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
sn (≥ 2.0.0), numDeriv (≥ 2016.8-1) |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2026-05-19 |
| DOI: |
10.32614/CRAN.package.smsncut (may not be active yet) |
| Author: |
Renato de Paula 
[aut, cre],
Helena Mouriño [aut],
Tiago Dias Domingues [aut] |
| Maintainer: |
Renato de Paula <rrpaula at ciencias.ulisboa.pt> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
smsncut results |
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