Keywords: Hierarchical likelihood, Hierarchical Generalized Linear Models, Statistical Modelling
Webpages:
https://CRAN.R-project.org/package=hglm,
https://CRAN.R-project.org/package=dhglm,
https://CRAN.R-project.org/package=mdhglm Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. We have summarized developments within the field and, using data examples, show how to analyse various kinds of data using
R. The work is currently being published as a monograph. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
