umx version 4.5: Extending Twin and Path-Based SEM in R with CLPM, MR-DoC, Definition Variables, $\Omega$nyx Integration, and Censored Distributions

TL;DR

umx v4.5 significantly enhances SEM capabilities for twin and longitudinal studies in R, introducing new models, improved data handling, and better integration with graphical tools to facilitate genetic and social science research.

Contribution

The paper introduces novel features in umx v4.5, including support for cross-lagged models, MR-DoC twin models, definition variables, and integration with Onyx, expanding SEM applications in behavioral genetics.

Findings

Added support for classic and modern cross-lagged panel models.

Implemented Mendelian Randomization Direction-of-Causation (MR-DoC) twin models.

Enhanced data handling with censored variables and covariate residualization.

Abstract

Structural Equation Modeling (SEM) is a flexible statistical technique with multiple applications, including behavioral genetics and social sciences. Building on the original design of the umx package, which improved accessibility to OpenMx by specifying a concise syntax, umx v4.5 extends functionality for longitudinal and causal twin designs while improving interoperability with graphical modelling tools such as Onyx. New capabilities include: classic and modern cross-lagged panel models; Mendelian Randomization Direction-of-Causation (MR-DoC) twin models incorporating polygenic scores as instruments; support for definition variables directly in umxRAM(); a workflow for importing paths from {\Omega}nyx; a dedicated function for incorporating censored variables' data into models, particularly valuable in biomarker research; improved covariate placeholder handling for definition variables; sex-limitation modelling across five twin groups, accommodating quantitative and qualitative sex differences; and covariate residualization in wide- or long-format data. These new functionalities accelerate reproducible, reliable, publication-ready twin and family modelling, and integrated journal-quality reporting, thereby lowering barriers to genetic epidemiological analyzes.