Dynamic Linear Models with R (Use R!)

This text introduces general state space models in detail before focusing on dynamic linear models, emphasizing their Bayesian analysis. It illustrates all the fundamental steps needed to use dynamic linear models in practice, using R.

• Time Series Analysis by State Space Methods: Second Edition (Oxford Statistical Science Series)
• Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)
• Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
• Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
• Time Series: Modeling, Computation, and Inference (Chapman & Hall/CRC Texts in Statistical Science)
• Hidden Markov Models for Time Series: An Introduction Using R, Second Edition (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
• Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
• Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)
• Generalized Additive Models: An Introduction with R, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
• Forecasting, Structural Time Series