Overview | Model 1: time varying intercept and slope | Model 2: time varying intercept and constant slope | Model 3: constant intercept and time varying slope | Comparing models

Introduction to the DFA model | DFA model with no extreme events | DFA model with extreme events | Fitting DFA models with non-Gaussian families | Alternative loadings for DFA models | Including autoregressive (AR) or moving-average (MA) components on trends | Applying Hidden Markov Models to identify latent regimes | DFA model with weights

Fish stomach contents example | Overdispersion or not? | Model selection | Summarizing estimates | Random effects

Example

Case 1: unequal trend variability | Candidate models | Recovering loadings | Recovering trends | Summary

Data simulation | Sampling argument | Posterior optimization | Posterior approximation

Data simulation | Estimating trends as B-splines | Estimating trends as P-splines | Estimating trends as Gaussian processes | Comparing approaches

Notation review for DFA models | Observation covariates | Process covariates | Examples -- observation covariates | Examples -- process covariates

2 - trend model | 3 - trend model

Overview | Simulating data from a VAR model | Fitting a model (and optional arguments) | Default priors | Shrinkage priors

Fish stomach contents example | Does scale of the data impact precision of the posterior estimates?

Dirichlet priors

Requirements | Simulating data | Ex 1: Linear food chain | Ex 2: Grazers & plants

Installation | Data | Fitting a univariate DLM | Fitting non-normal errors

Fitting time series models | Installing packages | Data | 1. Linear regression | Burn-in and thinning | 2. Linear regression with correlated errors | 3. Random walk model | 4. Autoregressive models | 5. State-space models