D
Douglas M. Bates
Researcher at University of Wisconsin-Madison
Publications - 80
Citations - 117051
Douglas M. Bates is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Generalized linear mixed model & Random effects model. The author has an hindex of 36, co-authored 80 publications receiving 88022 citations. Previous affiliations of Douglas M. Bates include Kansas State University & University of Alberta.
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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.
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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
Bioconductor: open software development for computational biology and bioinformatics
Robert Gentleman,Vincent J. Carey,Douglas M. Bates,Benjamin M. Bolstad,Marcel Dettling,Sandrine Dudoit,Byron Ellis,Laurent Gautier,Yongchao Ge,Jeff Gentry,Kurt Hornik,Torsten Hothorn,Wolfgang Huber,Stefano Maria Iacus,Rafael A. Irizarry,Friedrich Leisch,Cheng Li,Martin Maechler,A. J. Rossini,Günther Sawitzki,Colin A. Smith,Gordon K. Smyth,Luke Tierney,Jean Yang,Jianhua Zhang +24 more
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Book
Mixed-Effects Models in S and S-PLUS
TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
Linear Mixed-Effects Models using 'Eigen' and S4
TL;DR: The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen``glue''.