Multivariate (Dynamic) Generalized Additive Models
Calculate randomized quantile residuals for mvgam objects
Stan code and data objects for mvgam models
Display Conditional Effects of Predictors
Defining dynamic coefficients in mvgam formulae
Combine mvgam forecasts into evenly weighted ensembles
Evaluate forecasts from fitted mvgam objects
Expected Values of the Posterior Predictive Distribution
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 model
Specify dynamic Gaussian processes
Enhance mvgam post-processing using gratia functionality
Extract hindcasts for a fitted mvgam object
Index mvgam objects
Calculate latent VAR impulse response functions
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 mvgam latent factor loadings
MCMC plots as implemented in bayesplot
Extract model.frame from a fitted mvgam object
Monotonic splines in mvgam
Extract diagnostic quantities of mvgam models
Extract posterior draws from fitted mvgam objects
Supported mvgam families
mvgam_forecast object description
Details of formula specifications in mvgam
mvgam_irf object description
Helper functions for mvgam marginaleffects calculations
Supported mvgam trend models
Fitted mvgam object description
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
Pipe operator
Latent factor summaries for a fitted mvgam object
Plot mvgam posterior predictions for a specified series
Plot mvgam parametric term partial effects
Plot mvgam random effect terms
Residual diagnostics for a fitted mvgam object
Plot observed time series used for mvgam modelling
Plot mvgam smooth terms
Plot mvgam latent trend for a specified series
Plot mvgam forecast uncertainty contributions for a specified series
Plot impulse responses from an mvgam_irf object This function takes an...
Plot Pareto-k and ELPD values from a leave-future-out object
Default mvgam plots
Draws from the Expected Value of the Posterior Predictive Distribution
Posterior Draws of the Linear Predictor
Draws from the Posterior Predictive Distribution
Posterior Predictive Checks for mvgam Objects
Plot mvgam posterior predictive checks for a specified series
Predict from the GAM component of an mvgam model
Summary for a fitted mvgam object
Objects exported from other packages
Posterior draws of mvgam residuals
Specify autoregressive dynamic processes
Compute probabilistic forecast scores for mvgam objects
This function converts univariate or multivariate time series (xts o...
Simulate a set of time series for mvgam modelling
Summary for a fitted mvgam object
Update an existing mvgam object
Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic 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 (2022) <doi:10.1111/2041-210X.13974>.
Useful links