Generalized Linear Mixed-effects Models
Generalized Linear Mixed-Effects Models is a statistical modeling framework that extends generalized linear models by incorporating both fixed and random effects to account for variability across hierarchical or correlated data structures.
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2017–2026
937
Variants
Generalized Linear Mixed Effects Models
Parents
2.2K
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
Mixed-Effects Models in S and S-PLUS
Technometrics · 2001 · 9.4K citations · Full text
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University of California, Berkeley, United States
11 publications · 1.7K citations
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Harvard University
Cambridge, United States
34 h-index
187 publications
11.5K citations
University of Washington
Seattle, United States
32 h-index
121 publications
13.6K citations
University of Michigan
Ann Arbor, United States
30 h-index
117 publications
7.8K citations

Johns Hopkins University
Baltimore, United States
27 h-index
90 publications
20.4K citations
University of Pennsylvania
Philadelphia, United States
25 h-index
91 publications
3.7K citations
University of California, San Francisco
San Francisco, United States
23 h-index
96 publications
5.5K citations
University of Wisconsin–Madison
Madison, United States
21 h-index
40 publications
91.5K citations

University of British Columbia
Vancouver, Canada
21 h-index
66 publications
3.2K citations
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68 publications · 21.8K citations
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66 publications · 11.6K citations
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75 publications · 2.8K citations
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51 publications · 3.4K citations
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25 publications · 5.1K citations
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