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

Construct a MAR model | Fit state-space model | Compare to best fit model | Use a known observation error

Set up the data | Create the restriction matrix | Fit the model | Show output | Model with restrictions | re-run the analysis | run with a different search method | construct a MAR model using windows

Example data | Fit models without covariates | Log likelihoods | Compare parameter estimates | Add example with covariates | Fit model | Compare time and log likelihoods | More MARSS models | Run some time comparisons

Documentation | Tutorials | For Statisticians | CITATION | PUBLICATIONS | NOAA Disclaimer

tldr; | The MARSS model | Model specification | Data and fitting | Data | Fit call | Different fitting methods | Defaults for model list | form="marxss" | form="dfa" | Showing the model fits and getting the parameters | Tips and Troubleshooting | Tips | Troubleshooting | More information and tutorials | Shortcuts and all allowed model structures | Z | B | U and x0 | A | Q, R and V0 | D and C | d and c | G and H | Covariates, Linear constraints and time-varying parameters | Covariates | Linear constraints | Time-varying parameters

Include a comparison with covariates | Look at a bigger data set

Example | Parameter estimates | Estimated states | Fitted values | Diagnostics | Predictions and forecasts | Output to LaTeX | Important | Tips and Tricks | Linear constraints | Time-varying parameters | Need more information?

Example data | Fit models without covariates | Log likelihoods | Compare parameter estimates | Add example with covariates | Fit model | Parameter estimates | Plot estimates

Optimization | nlminb | optim | MARSSkem | Likelihood Calculation | Koopman and Durbin Kalman filter and smoother | Classic Kalman filter and smoother | TMB

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

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