diff --git a/NAMESPACE b/NAMESPACE
index c0f7474e..cb8df574 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -29,6 +29,7 @@ S3method(get_data,mvgam_prefit)
 S3method(get_predict,mvgam)
 S3method(get_vcov,mvgam)
 S3method(hindcast,mvgam)
+S3method(how_to_cite,mvgam)
 S3method(irf,mvgam)
 S3method(lfo_cv,mvgam)
 S3method(logLik,mvgam)
@@ -53,6 +54,7 @@ S3method(posterior_predict,mvgam)
 S3method(pp_check,mvgam)
 S3method(ppc,mvgam)
 S3method(predict,mvgam)
+S3method(print,how_to_cite)
 S3method(print,mvgam)
 S3method(print,mvgam_conditional_effects)
 S3method(print,mvgam_prefit)
@@ -111,6 +113,7 @@ export(get_mvgam_priors)
 export(get_predict)
 export(gp)
 export(hindcast)
+export(how_to_cite)
 export(hypotheses)
 export(irf)
 export(jsdgam)
diff --git a/R/how_to_cite.R b/R/how_to_cite.R
new file mode 100644
index 00000000..a0288856
--- /dev/null
+++ b/R/how_to_cite.R
@@ -0,0 +1,190 @@
+#' Generate a methods description for mvgam models
+#'
+#' Create a brief but fully referenced methods description, along with a useful list of references,
+#' for fitted \code{mvgam} and \code{jsdgam} models
+#'
+#'@name how_to_cite.mvgam
+#'@param object \code{list} object of class \code{mvgam} resulting from a call to [mvgam()]
+#' or [jsdgam()]
+#'@param ... 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.
+#'@return An object of class \code{how_to_cite} containing a text description of the
+#'methods as well as lists of primary and additional references
+#'@author Nicholas J Clark
+#'@seealso \code{\link[utils]{citation}}, \code{\link{mvgam}}, \code{\link{jsdgam}}
+#' @examples
+#' \donttest{
+#' # Simulate 4 time series with hierarchical seasonality
+#' # and a VAR(1) dynamic process
+#' set.seed(111)
+#' 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$data_train,
+#'               family = gaussian(),
+#'               trend_model = VAR(cor = TRUE),
+#'               noncentred = TRUE,
+#'               chains = 2)
+#' how_to_cite(mod1)
+#'
+#' # For a GP example, simulate data using the mgcv package
+#' dat <- mgcv::gamSim(1, n = 30, scale = 2)
+#'
+#' # Fit a model that uses an approximate GP from the brms package
+#' mod2 <- mvgam(y ~ gp(x2, k = 12),
+#'               data = dat,
+#'               family = gaussian(),
+#'               chains = 2)
+#' how_to_cite(mod2)
+#' }
+#'@export
+how_to_cite <- function(object, ...){
+  UseMethod("how_to_cite", object)
+}
+
+#'@export
+#'@export
+print.how_to_cite = function(x, ...){
+  cat('Methods text skeleton\n')
+  cat(x$methods_text, sep = '\n')
+  cat('\nPrimary references\n')
+  print(unlist(x$citations))
+  cat('\nOther useful references\n')
+  print(unlist(x$other_citations))
+  invisible(x)
+}
+
+#'@rdname how_to_cite.mvgam
+#'@method how_to_cite mvgam
+#'@export
+how_to_cite.mvgam <- function(object, ...){
+  current_year <- format(Sys.Date(), "%Y")
+  citations <- vector(mode = 'list')
+
+  # mvgam-specific methods
+  mvgam_text <- paste0(
+    "We used the R package mvgam (version ",
+    packageVersion("mvgam"),
+    "; Clark & Wells, 2023) to construct, fit and interrogate the model.",
+    " mvgam fits Bayesian State-Space models that can include flexible",
+    " predictor effects in both the process and observation components",
+    " by incorporating functionalities from the brms (Bürkner 2017),",
+    " mgcv (Wood 2017) and splines2 (Wang & Yan, 2023) packages."
+  )
+
+  citations[[1]] <- "Clark, NJ and Wells K (2022). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. Methods in Ecology and Evolution, 14, 771-784. doi.org/10.1111/2041-210X.13974"
+  citations[[2]] <- "Bürkner, PC (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01"
+  citations[[3]] <- "Wood, SN (2017). Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC."
+  citations[[4]] <- "Wang W and Yan J (2021). “Shape-Restricted Regression Splines with R Package splines2.” Journal of Data
+  Science, 19(3), 498-517. doi:10.6339/21-JDS1020 <https://doi.org/10.6339/21-JDS1020>."
+
+  # Stan-specific methods
+  citations[[5]] <- "Carpenter, B, Gelman, A, Hoffman, MD, Lee, D, Goodrich, B, Betancourt, M, Brubaker, M, Guo, J, Li, P and Riddell, A (2017). Stan: A probabilistic programming language. Journal of Statistical Software 76."
+
+  stan_text <-
+    paste0(
+      " We estimated posterior distributions for model parameters",
+      " with Hamiltonian Monte Carlo in Stan"
+    )
+
+  if(object$backend == 'cmdstanr'){
+    stan_text <- paste0(
+      stan_text,
+      " (version ",
+      cmdstanr::cmdstan_version(),
+      "; Carpenter et al. 2017, Stan Development Team ",
+      current_year,
+      "), specifically with the cmdstanr interface (Gabry & Češnovar, 2021)."
+    )
+    citations[[6]] <- paste0(
+      "Gabry J, Češnovar R, Johnson A, and Bronder S (",
+      current_year,
+      "). cmdstanr: R Interface to 'CmdStan'. https://mc-stan.org/cmdstanr/, https://discourse.mc-stan.org."
+    )
+  } else {
+    stan_text <- paste0(
+      stan_text,
+      " (version ",
+      rstan::stan_version(),
+      "; Carpenter et al. 2017)",
+      ", specifically with the rstan interface (Stan Development Team ",
+      current_year,
+      ")"
+    )
+    citations[[6]] <- paste0(
+      "Stan Development Team (",
+      current_year,
+      "). RStan: the R interface to Stan. R package version ",
+      packageVersion("rstan"),
+      ". https://mc-stan.org/."
+    )
+  }
+
+  # Any specials; first check whether this model used a VAR / VARMA process
+  specials_text <- NULL
+  trend_model <- attr(object$model_data, 'trend_model')
+  if(trend_model %in% c('VAR',
+                        'VARcor',
+                        'VARhiercor',
+                        'VAR1',
+                        'VAR1cor',
+                        'VAR1hiercor',
+                        'VARMA',
+                        'VARMAcor',
+                        'VARMA1,1cor')){
+    specials_text <- c(
+      specials_text,
+      " To encourage stability and prevent forecast variance from increasing indefinitely, we enforced stationarity of the Vector Autoregressive process following methods described in Heaps (2023)."
+    )
+    citations <- append(
+      citations,
+      list("Heaps, SE (2023). Enforcing stationarity through the prior in vector autoregressions. Journal of Computational and Graphical Statistics 32, 74-83.")
+    )
+  }
+
+  # Check for approximate GPs
+  if(!is.null(attr(object$mgcv_model, 'gp_att_table')) |
+     !is.null(attr(object$trend_mgcv_model, 'gp_att_table'))){
+    specials_text <- c(
+      specials_text,
+      " Gaussian Process functional effects were estimated using a low-rank Hilbert space approximation following methods described in Riutort-Mayol et al. (2023)."
+    )
+    citations <- append(
+      citations,
+      list("Riutort-Mayol, G, Bürkner, PC, Andersen, MR, Solin, A and Vehtari, A (2023). Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Statistics and Computing 33, 1. https://doi.org/10.1007/s11222-022-10167-2")
+    )
+  }
+
+  # Append texts
+  all_text <- paste0(mvgam_text, specials_text, stan_text)
+
+  # List of additional, possibly very useful references
+  other_citations <- vector(mode = 'list')
+  other_citations[[1]] <- "Arel-Bundock V (2024). marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests. R package version 0.19.0.4, <https://marginaleffects.com/>."
+  other_citations[[2]] <- "Gabry J, Simpson D, Vehtari A, Betancourt M, and Gelman A (2019). “Visualization in Bayesian workflow.” Journal of the Royal Statatistical Society A, 182, 389-402. doi:10.1111/rssa.12378."
+  other_citations[[3]] <- "Vehtari A, Gelman A, and Gabry J (2017). “Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.” Statistics and Computing, 27, 1413-1432. doi:10.1007/s11222-016-9696-4."
+  other_citations[[4]] <- "Bürkner, PC, Gabry, J, and Vehtari, A. (2020). Approximate leave-future-out cross-validation for Bayesian time series models. Journal of Statistical Computation and Simulation, 90(14), 2499–2523. https://doi.org/10.1080/00949655.2020.1783262"
+  other_citations[[5]] <- "Vehtari A, Gelman A, Simpson D, Carpenter B, and Bürkner P (2021). “Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion).” Bayesian Analysis 16(2) 667-718. https://doi.org/10.1214/20-BA1221."
+
+  out <- structure(
+    list(
+    methods_text = all_text,
+    citations = citations,
+    other_citations = other_citations
+  ),
+  class = 'how_to_cite'
+  )
+
+  return(out)
+}
+
+
+
+
diff --git a/R/summary.mvgam.R b/R/summary.mvgam.R
index bdf0596b..d8bbdba6 100644
--- a/R/summary.mvgam.R
+++ b/R/summary.mvgam.R
@@ -778,6 +778,10 @@ summary.mvgam = function(object,
     }
   }
 
+  mod_name <- as.character(match.call()$object)
+  cat(paste0('\nUse how_to_cite(',
+             mod_name,
+             ') to get started describing this model'))
 }
 
 #' @rdname summary.mvgam
diff --git a/man/how_to_cite.mvgam.Rd b/man/how_to_cite.mvgam.Rd
new file mode 100644
index 00000000..2a09d911
--- /dev/null
+++ b/man/how_to_cite.mvgam.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/how_to_cite.R
+\name{how_to_cite.mvgam}
+\alias{how_to_cite.mvgam}
+\alias{how_to_cite}
+\title{Generate a brief methods description text for mvgam models}
+\usage{
+how_to_cite(object, ...)
+
+\method{how_to_cite}{mvgam}(object, ...)
+}
+\arguments{
+\item{object}{\code{list} object of class \code{mvgam} resulting from a call to \code{\link[=mvgam]{mvgam()}}
+or \code{\link[=jsdgam]{jsdgam()}}}
+
+\item{...}{ignored}
+}
+\value{
+An object of class \code{how_to_cite} containing a text description of the
+methods as well as lists of primary and additional references
+}
+\description{
+Create fully referenced methods description, along with a useful list of references,
+for fitted \code{mvgam}
+}
+\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.
+}
+\examples{
+\donttest{
+# Simulate 4 time series with hierarchical seasonality
+# and a VAR(1) dynamic process
+set.seed(111)
+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),
+              noncentred = 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)
+}
+}
+\seealso{
+\code{\link[utils]{citation}}, \code{\link{mvgam}}, \code{\link{jsdgam}}
+}
+\author{
+Nicholas J Clark
+}