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Benjamin M. Bolker
Researcher at McMaster University
Publications - 159
Citations - 90175
Benjamin M. Bolker is an academic researcher from McMaster University. The author has contributed to research in topics: Population & Generalized linear mixed model. The author has an hindex of 57, co-authored 150 publications receiving 60042 citations. Previous affiliations of Benjamin M. Bolker include Princeton University & University of Cambridge.
Papers
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Journal ArticleDOI
Fitting Linear Mixed-Effects Models Using lme4
TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
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Fitting Linear Mixed-Effects Models using lme4
TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
Journal ArticleDOI
Generalized linear mixed models: a practical guide for ecology and evolution
Benjamin M. Bolker,Mollie Elizabeth Brooks,Connie J. Clark,Shane W. Geange,John R. Poulsen,M. Henry H. Stevens,Jada-Simone S. White +6 more
TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
Journal ArticleDOI
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
Mollie Elizabeth Brooks,Kasper Kristensen,Koen J. van Benthem,Arni Magnusson,Casper Willestofte Berg,Anders Nielsen,Hans J. Skaug,Martin Mächler,Benjamin M. Bolker +8 more
TL;DR: The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here the authors focus on count responses and its ability to estimate the Conway-Maxwell-Poisson distribution parameterized by the mean is unique.
Book
Ecological Models and Data in R
TL;DR: In step-by-step detail, Benjamin Bolker teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R.