Article
FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.
PLoS genetics - 1 Jun 2026
Parekh Pravesh, Parker Nadine, Pecheva Diliana, Frei Evgeniia, Vaudel Marc, Smith Diana M, Rigby Alison, Jahołkowski Piotr, Sønderby Ida Elken, Birkenæs Viktoria, Bakken Nora Refsum, Fan Chun Chieh, Makowski Carolina, Kopal Jakub, Loughnan Robert, Hagler Donald J, van der Meer Dennis, Johansson Stefan, Njølstad Pål Rasmus, Jernigan Terry L, Thompson Wesley K, Frei Oleksandr, Shadrin Alexey A, Nichols Thomas E, Andreassen Ole A, Dale Anders M
Abstract excerpt
While linear mixed-effects (LME) models are common for analyzing longitudinal data, most users rely on random intercepts or simple stationary covariance, due to unavailability of computationally tractable solutions. Here, we extend the Fast and Efficient Mixed-Effects Algorithm (FEMA) and present FEMA-Long, a computationally tractable approach to flexibly modeling longitudinal covariance suitable for...
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