B. M. Bolker's scientific contributions

Publications (3)

Technical Report
Full-text available
Description Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' ``glue''.

Citations

... Spatial independence of all datasets was examined using the Mantel test of the ade4 package (Dray and Dufour, 2007). To test the effects, generalized linear mixed models (GLMMs), generalized linear models (GLMs), linear mixed models (LMMs) or linear models (LMs) or using the lme4 package (Bates et al., 2014) (Table 1) were performed depending on model distributions tested in the car package (Fox and Weisberg, 2017) (Table 1) and where applicable, plantation estate was included as random parameter (Fig. 1). Post hoc tests were conducted using the multcomp package (Bretz et al., 2008). ...
... Estimated MFs and pairwise comparisons were obtained using the "glm" function in R, as described [27]. Estimated MFs by target were obtained using a generalized linear mixed model (GLMM) with a binomial error distribution performed by the "glmer" function of the "lme4" package [30] in R version 3.6.1. Pairwise comparisons based on dose, transcription status and chromatin state were estimated using an approach described by Soren and Halekoh, using the "doBy" R package [31]. ...
... [51] with the packages janitor [52], NeuroKit2 [43], broom. mixed [53], rstatix [54], reticulate [55], lme4 [56], lmerTest [57], emmeans [58], DHARMa [59], flextable [60], and the collection of packages tidyverse [61]. Summary statistics were reported as means (M) and confidence intervals (CI), and visualized as boxplots. ...