Halva—‘grapHical Analysis with Latent VAriables’—is a Python package dedicated to statistical analysis of multivariate ordinal data, designed specifically to handle missing values and latent variables ...

ABSTRACT: This study investigates the application of cumulative link models with alternative distributions (hyperbolic secant, Laplace, and Cauchy) to model ordinal outcomes of depressive severity ...

Dataset: survey ratings (1–10 scale) Target variable: Writing Methods: CUB (in R), Proportional Odds Model (in Python) Goal: Compare model adequacy and interpret ordinal responses ...

The volume of data generated by logs and incident alerts is nothing short of overwhelming. But for security operations teams, sifting through it to identify and mitigate potential threats makes the ...

ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...

Animal welfare is typically assessed using ordinal scales. That is, standard welfare assessment tools rank conditions relative to one another without claiming that one condition is worse than another ...

Abstract: This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordinal ...

Several methods for conducting power analysis of studies with outcomes across the full ordinal modified Rankin Scale are proposed in the literature. No systematic comparison of accuracy, agreement, ...