Multivariate (Dynamic) Generalized Additive Models
Calculate randomized quantile residuals for mvgam objects
Augment an mvgam object's data
Stan code and data objects for mvgam models
Display conditional effects of predictors for mvgam models
Defining dynamic coefficients in mvgam formulae
Combine forecasts from mvgam models into evenly weighted ensembles
Evaluate forecasts from fitted mvgam objects
Calculate latent VAR forecast error variance decompositions
Expected values of the posterior predictive distribution for mvgam o...
Extract or compute hindcasts and forecasts for a fitted mvgam object
Extract formulae from mvgam objects
Extract information on default prior distributions for an mvgam mode...
Specify dynamic Gaussian process trends in mvgam models
Enhance post-processing of mvgam models using gratia functionality
Extract hindcasts for a fitted mvgam object
Generate a methods description for mvgam models
Index mvgam objects
Calculate latent VAR impulse response functions
Fit Joint Species Distribution Models in mvgam
Approximate leave-future-out cross-validation of fitted mvgam object...
Compute pointwise Log-Likelihoods from fitted mvgam objects
LOO information criteria for mvgam models
Calculate trend correlations based on latent factor loadings for `mvga...
MCMC plots of mvgam parameters, as implemented in bayesplot
Extract model.frame from a fitted mvgam object
Monotonic splines in mvgam models
Extract diagnostic quantities of mvgam models
Extract posterior draws from fitted mvgam objects
Supported mvgam families
mvgam_fevd object description
mvgam_forecast object description
Details of formula specifications in mvgam models
mvgam_irf object description
Helper functions for marginaleffects calculations in mvgam models
Supported latent trend models in mvgam
Fitted mvgam object description
mvgam: Multivariate (Dynamic) Generalized Additive Models
Fit a Bayesian dynamic GAM to a univariate or multivariate set of time...
Create a matrix of output plots from a mvgam object
Specify piecewise linear or logistic trends in mvgam models
Pipe operator
Latent factor summaries for a fitted mvgam object
Plot posterior forecast predictions from mvgam models
Plot parametric term partial effects for mvgam models
Plot random effect terms from mvgam models
Residual diagnostics for a fitted mvgam object
Plot observed time series used for mvgam modelling
Plot smooth terms from mvgam models
Plot latent trend predictions from mvgam models
Plot forecast uncertainty contributions from mvgam models
Plot forecast error variance decompositions from an mvgam_fevd objec...
Plot impulse responses from an mvgam_irf object
Plot Pareto-k and ELPD values from a mvgam_lfo object
Plot residual correlations based on latent factors from a fitted jsdga...
Default plots for mvgam models
Draws from the expected value of the posterior predictive distribution...
Posterior draws of the linear predictor for mvgam objects
Draws from the posterior predictive distribution for mvgam objects
Posterior Predictive Checks for mvgam models
Plot conditional posterior predictive checks from mvgam models
Predict from a fitted mvgam model
Summary for a fitted mvgam object
Objects exported from other packages
Extract residual correlations based on latent factors from a fitted js...
Posterior draws of residuals from mvgam models
Specify autoregressive dynamic processes in mvgam
Compute probabilistic forecast scores for mvgam models
Convert timeseries object to format necessary for mvgam models
Simulate a set of time series for modelling in mvgam
Calculate measures of latent VAR community stability
Posterior summary of forecast error variance decompositions
Posterior summary of impulse responses
Summary for a fitted mvgam models
Update an existing mvgam model object
Specify correlated residual processes in mvgam
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Useful links