Summary
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) <<a href="https://doi.org/10.1007%2Fs11336-013-9328-2" target="_top">doi:10.1007/s11336-013-9328-2</a>>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <<a href="https://doi.org/10.18637%2Fjss.v067.i01" target="_top">doi:10.18637/jss.v067.i01</a>>).
Outcomes reported
Referenced by Nature Communications British biodiversity scenarios as citation 91; likely supports topic area: methods / modelling / statistics. Topics: methods / modelling / statistics Evidence type: Modelling / projection Source report: Nature Communications British biodiversity scenarios Ref#: Nature Communications British biodiversity scenarios #91 Original: Dorie, V. blme: Bayesian Linear Mixed-Effects Models (Version 1.0- 5) https://cran.r-project.org/web/packages/blme/index.html (2021).
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