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cff-version: 1.2.0
message: 'To cite package "smsncut" in publications use:'
type: software
license: GPL-3.0-only
title: 'smsncut: Optimal Diagnostic Cutoff Selection under Scale Mixtures of Skew-Normal
  Distributions'
version: 0.1.0
abstract: 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) <https://doi.org/10.48550/arXiv.2605.07829>.
authors:
- family-names: Paula
  given-names: Renato
  name-particle: de
  email: rrpaula@ciencias.ulisboa.pt
  orcid: https://orcid.org/0000-0002-5835-5918
- family-names: Mouriño
  given-names: Helena
- family-names: Dias Domingues
  given-names: Tiago
repository: https://cran.r-universe.dev
commit: df95c3f1321c3d9a33b2a0582fae0f6b1bc5c401
date-released: '2026-05-19'
contact:
- family-names: Paula
  given-names: Renato
  name-particle: de
  email: rrpaula@ciencias.ulisboa.pt
  orcid: https://orcid.org/0000-0002-5835-5918