Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimble, Stan and TMB provides an important guide and comparison of the powerful new software packages, such as JAGS, Stan, Nimble, and TMB, that are now widely used in research publications. In addition, this book is simple and accessible, allowing researchers to carry out and understand statistical modeling, provides a gentle introduction to the most exciting, specialist software with which this "statistical learning from data" is often done in current cutting-edge research along with Bayesian statistics and frequentist statistics with its maximum likelihood estimation method. Though the examples in Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimble, Stan and TMB will come from ecology and environmental science, the underlying statistical models are widely used by scientists across many disciplines. Thus, this book will be useful for anybody who needs to learn and to quickly become proficient in statistical modeling and in the model-fitting engines covered. Comprehensive, applied introduction to what currently are some of the most exciting, cutting-edge model fitting software packages: JAGS, Nimble, Stan, and TMB Covers all the basics of the modern applied statistical modeling that have become a key part of any natural science nowadays: linear, generalized linear, mixed and also hierarchical models Provides applied introduction to the two dominant methods of parametric statistical modeling: maximum likelihood and Bayesian inference Adopts what could be called a "Rosetta stone approach", wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in one of the other engines Contains a concise and gentle introduction to those fundamentals in probability and applied statistics
