how_to_cite.mvgam: Generate a methods description for 'mvgam' models

In mvgam: Multivariate (Dynamic) Generalized Additive Models

Generate a methods description for mvgam models

Description

Create a brief but fully referenced methods description, along with a useful list of references, for fitted mvgam and jsdgam models.

Usage

how_to_cite(object, ...)

## S3 method for class 'mvgam'
how_to_cite(object, ...)

Arguments

object	list object of class mvgam resulting from a call to mvgam() or jsdgam()
...	ignored

Details

This function uses the model's structure to come up with a very basic but hopefully useful methods description that can help users to appropriately acknowledge the hard work of developers and champion open science. Please do not consider the text returned by this function to be a completely adequate methods section; it is only meant to get you started.

Value

An object of class how_to_cite containing a text description of the methods as well as lists of both primary and additional references.

Author(s)

Nicholas J Clark

See Also

citation, mvgam, jsdgam

Examples

## Not run: 
#--------------------------------------------------
# Simulate 4 time series with hierarchical seasonality
# and a VAR(1) dynamic process
#--------------------------------------------------
set.seed(0)

simdat <- sim_mvgam(
  seasonality = 'hierarchical',
  trend_model = VAR(cor = TRUE),
  family = gaussian()
)

# Fit an appropriate model
mod1 <- mvgam(
  y ~ s(season, bs = 'cc', k = 6),
  data = simdat$data_train,
  family = gaussian(),
  trend_model = VAR(cor = TRUE),
  chains = 2,
  silent = 2
)

how_to_cite(mod1)

#--------------------------------------------------
# For a GP example, simulate data using the mgcv package
#--------------------------------------------------
dat <- mgcv::gamSim(1, n = 30, scale = 2)

# Fit a model that uses an approximate GP from brms
mod2 <- mvgam(
  y ~ gp(x2, k = 12),
  data = dat,
  family = gaussian(),
  chains = 2,
  silent = 2
)

how_to_cite(mod2)

## End(Not run)

mvgam documentation built on Jan. 21, 2026, 9:07 a.m.