Youngjo Lee is a professor in the department of Statistics at Seoul National University, Korea. His current research interests are extension, application, theory and software developments for HGLMs.Lars Rönnegård is affiliated with the Microdata Analysis group at Dalarna University, Sweden. His current research interests are applications of HGLMs in genetics and ecology, and computational aspects.Maengseok Noh is a professor in the Department of Statistics at Pukyong National University, Korea. His current research interests are application and software developments for HGLMs.

"Data Analysis Using Hierarchical Generalized Linear Models with R by Lee et al is an advanced book on regression and mixed effects statistical models. The book presents a class of generalized linear models (GLMs) with random effects. In hierarchical generalized linear models (HGLMs), the random effects might enter in the location parameter, in the dispersion parameter, or in both. These extensions cover a vast number of statistical problems containing unobservable random variables, including missing data, latent variables, and predictions. The book presents an endless volume of case studies, using a bundle of R packages for implementation: hglm, dhglm, mdhglm, frailtyHL and jointdhglm. …The authors concentrate on the practical aspects of HGLMs, and show how improvements in numerical methods (e.g., Laplace approximations to integrals) allow HGLMs to be used in practice. The diversity of statistical models covered in this book is fascinating. ...In general, the authors have presented a good balance between theory and practical applications in R."-Pablo Emilio Verde, ISCB Jun2 2018

• Introduction. GLMs via iterative weighted least squares. Inference for models with unobservables. HGLMs: from Method to Algorithm. HGLM modelling in R. Double HGLMS - using the dhglm package. Fitting multivariate HGLMs.Survival analysis.Joint models.Further Topics.

Youngjo Lee is a professor in the department of Statistics at Seoul National University, Korea. His current research interests are extension, application, theory and software developments for HGLMs.Lars Rönnegård is affiliated with the Microdata Analysis group at Dalarna University, Sweden. His current research interests are applications of HGLMs in genetics and ecology, and computational aspects.Maengseok Noh is a professor in the Department of Statistics at Pukyong National University, Korea. His current research interests are application and software developments for HGLMs.

"Data Analysis Using Hierarchical Generalized Linear Models with R by Lee et al is an advanced book on regression and mixed effects statistical models. The book presents a class of generalized linear models (GLMs) with random effects. In hierarchical generalized linear models (HGLMs), the random effects might enter in the location parameter, in the dispersion parameter, or in both. These extensions cover a vast number of statistical problems containing unobservable random variables, including missing data, latent variables, and predictions. The book presents an endless volume of case studies, using a bundle of R packages for implementation: hglm, dhglm, mdhglm, frailtyHL and jointdhglm. …The authors concentrate on the practical aspects of HGLMs, and show how improvements in numerical methods (e.g., Laplace approximations to integrals) allow HGLMs to be used in practice. The diversity of statistical models covered in this book is fascinating. ...In general, the authors have presented a good balance between theory and practical applications in R."-Pablo Emilio Verde, ISCB Jun2 2018

• Introduction. GLMs via iterative weighted least squares. Inference for models with unobservables. HGLMs: from Method to Algorithm. HGLM modelling in R. Double HGLMS - using the dhglm package. Fitting multivariate HGLMs.Survival analysis.Joint models.Further Topics.