Generate a methods description for mvgam models

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 mvgam
how_to_cite(object, ...)

Arguments

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

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

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.

Author

Nicholas J Clark

Examples

if (FALSE) {
# 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)
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 the brms package
mod2 <- mvgam(y ~ gp(x2, k = 12),
              data = dat,
              family = gaussian(),
              chains = 2)
how_to_cite(mod2)

# Repeat using meanfield variational inference
mod3 <- mvgam(y ~ gp(x2, k = 12),
              data = dat,
              family = gaussian(),
              algorithm = 'meanfield')
how_to_cite(mod3)
}