The trend_map argument | Checking trend_map with run_model = FALSE | Fitting and inspecting the model | Example: signal detection | The shared signal model | Inspecting effects on both process and observation models | Recovering the hidden signal | Further reading | Interested in contributing?

State-Space Models | Lake Washington plankton data | Capturing seasonality | Multiseries dynamics | process model formula, which includes the smooth functions | VAR1 model with uncorrelated process errors | Inspecting SS models | Correlated process errors | Impulse response functions | Comparing forecast scores | Further reading | Interested in contributing?

Time-varying effects | Simulating time-varying effects | The dynamic() function | Salmon survival example | A State-Space Beta regression | Including time-varying upwelling effects | Comparing model predictive performances | Further reading | Interested in contributing?

Required tidy data format | series as a factor variable | A single outcome variable | A time variable | Irregular sampling intervals? | Checking data with get_mvgam_priors() | Covariates with no NAs | Plotting with plot_mvgam_series() | Example with NEON tick data | Further reading | Interested in contributing?

Dynamic GAMs | Supported observation families | Supported temporal dynamic processes | Correlated multivariate processes | Independent Random Walks | Multivariate Random Walks | Autoregressive processes | Vector Autoregressive processes | Hierarchical processes | Gaussian Processes | Piecewise logistic and linear trends | Continuous time AR(1) processes | Regression formulae | Example time series data | Manipulating data for modelling | GLMs with temporal random effects | Plotting effects and residuals | bayesplot support | Automatic forecasting for new data | Adding predictors as "fixed" effects | marginaleffects support | Adding predictors as smooths | Latent dynamics in mvgam | Further reading | Interested in contributing?

Simulating discrete time series | Modelling dynamics with splines | Modelling dynamics with a correlated AR1 | Forecasting with the forecast() function | Forecasting with newdata in mvgam() | Scoring forecast distributions | Further reading | Interested in contributing?

N-mixture models | Example 1: a two-species system with nonlinear trends | Setting up the trend_map | Modelling with the nmix() family | Example 2: a larger survey with possible nonlinear effects | Further reading | Interested in contributing?

<<<<<<< HEAD | title: "MRFs and CRFs for Bird.parasites data"output: rmarkdown::html_vignettevignette: >%\VignetteIndexEntry | Running MRFs and visualising interaction coefficients | Running CRFs using additional covariates | Comparing fits of MRF and CRF models | Bootstrapping the data and running models | Exploring regression coefficients and interpreting results | Accounting for possible spatial autocorrelation | =======

Running CRFs with categorical covariates requires expansion to model matrix format