Books

This book discuss some of the methodological background that is behind R-INLA

Errata and Comments received from Readers.

A practical book focusing on advanced spatial modeling using SPDE's and R-INLA

All the examples in the book are fully reproducible.

GitBook
zip-file with code
Hardback copies of the book are available for pre-order from the CRC website.

A gentle general and applied introduction.

All examples in the book are fully reproducible and datasets and R code are available from the book website.

GitBook
zip-file with code

``This book introduces the basic paradigms of the Bayesian approach and describes the associated computational issues. Detailing the theory behind the INLA approach and the R-INLA package, it focuses on spatial and spatio-temporal modeling for area and point-referenced data.''

Web-page for the book

``The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.''

Web-page for the book with code and data
Book is here.

``The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology.''

``In Volume I we explain how to apply linear regression models, generalised linear models (GLM), and generalised linear mixed-effects models (GLMM) to spatial, temporal, and spatial-temporal data. The models that will be employed use the Gaussian and gamma distributions for continuous data, the Poisson and negative binomial distributions for count data, the Bernoulli distribution for absence–presence data, and the binomial distribution for proportional data.''

``In Volume II we apply zero-inflated models and generalised additive (mixed-effects) models to spatial and spatial-temporal data.''

Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples.

Online version is available here.

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