The rapid expansion of healthcare data stored in electronic health records (EHRs) has created new opportunities for biomedical research, enabling the study of complex, heterogeneous, and longitudinal clinical information. However, extracting meaningful relationships among clinical variables remains challenging due to high dimensionality, temporal dependence, and the presence of confounding factors. Traditional approaches, such as Gaussian Graphical Models and Vector Autoregression, are often limited by strong assumptions, including independence and stationarity, which restrict their applicability to real-world EHR data.

To address these challenges, we present MariNET, an R package designed for the construction and analysis of networks derived from longitudinal clinical data. MariNET is based on linear mixed models, allowing the inference of interactions between clinical variables while explicitly accounting for repeated measures, subject-level variability, and confounding effects. The package provides a flexible and scalable framework for modeling dynamic relationships, supporting both continuous and categorical variables, as well as unbalanced and incomplete datasets commonly found in EHRs.

MariNET includes a comprehensive set of functionalities for data preprocessing, model fitting, and network construction, enabling users to estimate associations between variables over time and represent them as interpretable network structures. The package further facilitates customization of model specifications, including the incorporation of fixed and random effects, and offers tools for assessing statistical significance and robustness of inferred interactions. Designed with usability in mind, MariNET integrates seamlessly within the R ecosystem, providing an accessible interface for researchers without requiring advanced expertise in statistical modeling.

Co-authors: Marina Vargas-Fernandez, Jordi Martorell-Marugán