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The key feature of lme4 is multi-level modeling, allowing us to invoke species as a random effect (Gelman and Hill [2007]).
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Additionally, modeling allows us to assign exposures specific to biennially updated residential addresses for the entire period of follow-up.
Agent-based modeling allows us to study the resulting global performance metrics of the simulator kernel.
Additionally, protein modeling allowed us to relate the results of phylogenetic studies with the predicted structures.
We developed a random-walk-based model, allowing us to accurately reproduce the empirical observations.
We aimed to produce a descriptive model allowing us summarizing data on adverse events statistically.
We developed a model allowing us to correct for chromosome dosage in subsequent analyses (see Methods).
We have developed two clinically relevant large-animal models allowing us to study the renal responses to sepsis.
This model allows us to use IVC to carotid artery transplants between congenic mice.
However, using GEE models allowed us to analyze data from all 143 subject visits, and we identified several significant associations.
The use of similarity-based models allowed us to obtain better positive predictive values in some sets.
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