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An Introduction to Linear Mixed Effects (LME) models

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The use of linear mixed-effect models is becoming increasingly popular in neuroimaging. These models extend the simple linear models by allowing both fixed and random effects, and are particularly useful for clustered and hierarchical data. In this upcoming session, Delia will introduce the concept of linear mixed-effect models and discuss when they should be used. Roni will demonstrate how these are implemented with R. Alex Q will review some advanced topics, including estimation of parameters (family parameters and random effects) as well as the use of Bayesian statistics.

This talk is part of the Imagers Interest Group series.

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