smsnpycut

A small Python port of the R package smsncut: decision-theoretic optimal cutoff selection for a continuous diagnostic biomarker, modeled with scale mixtures of skew-normal (SMSN) distributions (Skew-Normal and Skew-t).

Given measurements from a healthy and a diseased group, it fits SMSN models by MLE and finds the cutoff that minimizes a weighted misclassification risk (accounting for disease prevalence and asymmetric false-positive/false-negative costs) — rather than just the symmetric Youden index. It also gives asymptotic confidence intervals for the cutoff, ROC/AUC, and Monte Carlo validation.

Install

uv sync

Quick start

import numpy as np
import smsnpycut as sc

rng = np.random.default_rng(0)
healthy = sc.SkewNormal(xi=0.0, omega=1.0, alpha=2.0)
diseased = sc.SkewNormal(xi=2.5, omega=1.2, alpha=-1.0)

fit0 = sc.fit(healthy.rvs(300, random_state=rng), family="SN")
fit1 = sc.fit(diseased.rvs(300, random_state=rng), family="SN")

# 70% prevalence of healthy, false negatives 3x costlier than false positives
c_opt = sc.optimal_cutoff(fit0.dist, fit1.dist, pi0=0.7, pi1=0.3, lam0=1.0, lam1=3.0)
ci = sc.confidence_interval(c_opt, sc.variance(c_opt, fit0, fit1, 0.7, 0.3, 1.0, 3.0))
print(c_opt, ci, sc.auc(fit0.dist, fit1.dist))

See main.py for a full worked example.

API

Citation

See CITATION.bib for the original R package and its companion paper (de Paula, Mouriño & Dias Domingues, 2026).