"This book is a welcome addition to the series Use R! The text is interspersed with snippets of R code to illustrate the techniques and models and provides the basis of an excellent text for private study." (International Statistical Review, 2010, 78, 1, 134-159) "Dynamic Linear models With R provides a friendly introduction to the world of dynamic linear models (DLMs)... . This book provides the reader with the minimal tools necessary for Bayesian analysis of time series data using DLMs. ...The main contribution...is the DLM package in R which provides functions for dynamic linear model creation as well as filtering, smoothing, and forecasting. Therefore, the book can be utilized as a descriptive manual that provides a hybrid practical-theoretical perspective on the purpose of the functions in this extremely useful R package. ...I'd like to thank these authors for a useful applied Bayesian time series handbook suitable to a graduate statistics course and also to thank the editors of the Use R! series for providing a valuable collection of books for a fantastic open-source software." (American Statistician, August 2010, Vol. 64, No. 3)

• Introduction: basic notions about Bayesian inference.- Dynamic linear models.- Model specification.- Models with unknown parameters.- Sequential Monte Carlo methods.

"This book is a welcome addition to the series Use R! The text is interspersed with snippets of R code to illustrate the techniques and models and provides the basis of an excellent text for private study." (International Statistical Review, 2010, 78, 1, 134-159) "Dynamic Linear models With R provides a friendly introduction to the world of dynamic linear models (DLMs)... . This book provides the reader with the minimal tools necessary for Bayesian analysis of time series data using DLMs. ...The main contribution...is the DLM package in R which provides functions for dynamic linear model creation as well as filtering, smoothing, and forecasting. Therefore, the book can be utilized as a descriptive manual that provides a hybrid practical-theoretical perspective on the purpose of the functions in this extremely useful R package. ...I'd like to thank these authors for a useful applied Bayesian time series handbook suitable to a graduate statistics course and also to thank the editors of the Use R! series for providing a valuable collection of books for a fantastic open-source software." (American Statistician, August 2010, Vol. 64, No. 3)

• Introduction: basic notions about Bayesian inference.- Dynamic linear models.- Model specification.- Models with unknown parameters.- Sequential Monte Carlo methods.