BioMixModel: Mixed Models for Biological, Clustered and Longitudinal Data
Fits and interprets mixed-effects models for clustered,
longitudinal and heterogeneous biological data. Provides variance
partitioning, intraclass correlation, penalized likelihood summaries,
a heterogeneous-data information criterion, model comparison,
diagnostics, and ensemble-style summaries for multilevel data. The
package is designed as a complementary, interpretable workflow around
established mixed-model methods. Methods for intraclass correlation
and variance partitioning are informed by Nakagawa and Schielzeth
(2010) <doi:10.1111/j.1469-185X.2010.00141.x> and Nakagawa et al.
(2017) <doi:10.1098/rsif.2017.0213>. Mixed-effects modeling
approaches are described by Zuur et al. (2009)
<doi:10.1007/978-0-387-87458-6>.
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