diff --git a/R/conditional_effects.R b/R/conditional_effects.R
index b0b759ec..116dd70b 100644
--- a/R/conditional_effects.R
+++ b/R/conditional_effects.R
@@ -35,6 +35,7 @@
 #'   and create more bespoke conditional effects plots.
 #' @name conditional_effects.mvgam
 #' @author Nicholas J Clark
+#' @seealso \code{\link[marginaleffects]{plot_predictions}}, \code{\link[marginaleffects]{plot_slopes}}
 #' @examples
 #' \dontrun{
 #' # Simulate some data
diff --git a/R/forecast.mvgam.R b/R/forecast.mvgam.R
index 59dfdc39..90b9047c 100644
--- a/R/forecast.mvgam.R
+++ b/R/forecast.mvgam.R
@@ -3,7 +3,7 @@
 #'@importFrom parallel clusterExport stopCluster setDefaultCluster
 #'@importFrom stats predict
 #'@importFrom rlang missing_arg
-#'@param object \code{list} object returned from \code{mvgam}. See [mvgam()]
+#'@inheritParams predict.mvgam
 #'@param newdata Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
 #'in addition to any other variables included in the linear predictor of the original \code{formula}. If included, the
 #'covariate information in \code{newdata} will be used to generate forecasts from the fitted model equations. If
@@ -19,17 +19,11 @@
 #'if the fitted model contained multivariate trends (either as a dynamic factor or \code{VAR} process),
 #'as it saves recomputing the full set of trends for each series individually
 #'@param n_cores \code{integer} specifying number of cores for generating forecasts in parallel
-#'@param type When this has the value \code{link} the linear predictor is calculated on the link scale.
-#'If \code{expected} is used, predictions reflect the expectation of the response (the mean)
-#'but ignore uncertainty in the observation process. When \code{response} (default) is used,
-#'the predictions take uncertainty in the observation process into account to return
-#'predictions on the outcome scale. When \code{trend} is used, only the forecast distribution for the
-#'latent trend is returned
 #'@param ... Ignored
 #'@details Posterior predictions are drawn from the fitted \code{mvgam} and used to simulate a forecast distribution
 #'@return An object of class \code{mvgam_forecast} containing hindcast and forecast distributions.
 #'See \code{\link{mvgam_forecast-class}} for details.
-#'
+#'@seealso \code{\link{hindcast}}, \code{\link{score}}
 #'@export
 forecast <- function(object, ...){
   UseMethod("forecast", object)
diff --git a/R/formula.mvgam.R b/R/formula.mvgam.R
index 98814486..f99e7d85 100644
--- a/R/formula.mvgam.R
+++ b/R/formula.mvgam.R
@@ -1,13 +1,13 @@
-#'Extract model.frame from a fitted mvgam object
+#'Extract formulae from mvgam objects
 #'
 #'@rdname formula.mvgam
 #'@param x `mvgam` or `mvgam_prefit` object
-#'@param trend_effects \code{logical}, return the model.frame from the
+#'@param trend_effects \code{logical}, return the formula from the
 #'observation model (if \code{FALSE}) or from the underlying process
 #'model (if\code{TRUE})
 #'@param ... Ignored
 #'@author Nicholas J Clark
-#'@return A \code{matrix} containing the fitted model frame
+#'@return A \code{formula} object
 #'@export
 formula.mvgam = function(x, trend_effects = FALSE, ...){
   # Check trend_effects
diff --git a/R/get_mvgam_priors.R b/R/get_mvgam_priors.R
index d6d2c8a5..c058e90d 100644
--- a/R/get_mvgam_priors.R
+++ b/R/get_mvgam_priors.R
@@ -4,13 +4,6 @@
 #'changed for a given `mvgam` model, as well listing their default distributions
 #'
 #' @inheritParams mvgam
-#'@param use_stan Logical. If \code{TRUE} and if \code{rstan} is installed, the model will be compiled and sampled using
-#'the Hamiltonian Monte Carlo with a call to \code{\link[cmdstanr]{cmdstan_model}} or, if `cmdstanr` is not available,
-#'a call to \code{\link[rstan]{stan}}. Note that this functionality is still in development and
-#'not all options that are available in \code{JAGS} can be used, including: no option for a Tweedie family and no option for
-#'dynamic factor trends. However, as \code{Stan} can estimate Hilbert base approximate Gaussian Processes, which
-#'are much more computationally tractable than full GPs for time series with `>100` observations, estimation
-#'in \code{Stan} can support latent GP trends while estimation in \code{JAGS} cannot
 #'@details Users can supply a model formula, prior to fitting the model, so that default priors can be inspected and
 #'altered. To make alterations, change the contents of the `prior` column and supplying this
 #'\code{data.frame} to the `mvgam` function using the argument `priors`. If using `Stan` as the backend,
@@ -26,7 +19,7 @@
 #' ensure that the code is legal (i.e. to check that lower bounds are smaller than upper bounds, for
 #' example)
 #'@author Nicholas J Clark
-#'@seealso \code{\link{mvgam}}
+#'@seealso \code{\link{mvgam}} \code{\link[brms]{prior}}
 #'@return either a \code{data.frame} containing the prior definitions (if any suitable
 #'priors can be altered by the user) or \code{NULL}, indicating that no priors in the model
 #'can be modified through the `mvgam` interface
diff --git a/R/hindcast.mvgam.R b/R/hindcast.mvgam.R
index f6ecd968..cce003d0 100644
--- a/R/hindcast.mvgam.R
+++ b/R/hindcast.mvgam.R
@@ -1,23 +1,17 @@
 #'@title Extract hindcasts for a fitted \code{mvgam} object
 #'@name hindcast.mvgam
 #'@importFrom stats predict
-#'@param object \code{list} object returned from \code{mvgam}. See [mvgam()]
+#'@inheritParams predict.mvgam
 #'@param series Either a \code{integer} specifying which series in the set is to be forecast,
 #'or the character string \code{'all'}, specifying that all series should be forecast. This is preferable
 #'if the fitted model contained multivariate trends (either as a dynamic factor or \code{VAR} process),
 #'as it saves recomputing the full set of trends for each series individually
-#'@param type When this has the value \code{link} the linear predictor is calculated on the link scale.
-#'If \code{expected} is used, predictions reflect the expectation of the response (the mean)
-#'but ignore uncertainty in the observation process. When \code{response} (default) is used,
-#'the predictions take uncertainty in the observation process into account to return
-#'predictions on the outcome scale. When \code{trend} is used, only the hindcast distribution for the
-#'latent trend is returned
 #'@param ... Ignored
 #'@details Posterior retrodictions are drawn from the fitted \code{mvgam} and
 #'organized into a convenient format
 #'@return An object of class \code{mvgam_forecast} containing hindcast distributions.
 #'See \code{\link{mvgam_forecast-class}} for details.
-#'
+#'#'@seealso \code{\link{forecast.mvgam}}
 #'@export
 hindcast <- function(object, ...){
   UseMethod("hindcast", object)
diff --git a/R/index-mvgam.R b/R/index-mvgam.R
index 0d0b1469..0b2c77df 100644
--- a/R/index-mvgam.R
+++ b/R/index-mvgam.R
@@ -12,7 +12,7 @@ NULL
 
 #' @rdname index-mvgam
 #' @importFrom posterior variables
-#' @param x \code{list} object of class `mvgam`
+#' @param x \code{list} object returned from \code{mvgam}. See [mvgam()]
 #' @method variables mvgam
 #' @export
 #' @export variables
diff --git a/R/mcmc_plot.mvgam.R b/R/mcmc_plot.mvgam.R
index 6667bc82..3b5331a7 100644
--- a/R/mcmc_plot.mvgam.R
+++ b/R/mcmc_plot.mvgam.R
@@ -9,15 +9,18 @@
 #' @param type The type of the plot.
 #'   Supported types are (as names) \code{hist}, \code{dens},
 #'   \code{hist_by_chain}, \code{dens_overlay},
-#'   \code{violin}, \code{intervals}, \code{areas}, \code{acf},
-#'   \code{acf_bar},\code{trace}, \code{trace_highlight}, \code{scatter},
+#'   \code{violin}, \code{intervals}, \code{areas},
+#'   \code{areas_ridges}, \code{combo}, \code{acf},
+#'   \code{acf_bar}, \code{trace}, \code{trace_highlight},
+#'   \code{scatter}, \code{hex}, \code{pairs}, \code{violin},
 #'   \code{rhat}, \code{rhat_hist}, \code{neff}, \code{neff_hist}
 #'   and \code{nuts_energy}.
 #'   For an overview on the various plot types see
 #'   \code{\link[bayesplot:MCMC-overview]{MCMC-overview}}.
 #' @return A \code{\link[ggplot2:ggplot]{ggplot}} object
 #' that can be further customized using the \pkg{ggplot2} package.
-#'
+#' @seealso \code{\link{mvgam_draws}} for an overview of some of the shortcut strings
+#' that can be used for argument `variable`
 #' @examples
 #' \dontrun{
 #' simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
@@ -26,6 +29,8 @@
 #'             data = simdat$data_train)
 #' mcmc_plot(mod)
 #' mcmc_plot(mod, type = 'neff_hist')
+#' mcmc_plot(mod, variable = 'betas', type = 'areas')
+#' mcmc_plot(mod, variable = 'trend_params', type = 'combo')
 #' }
 #' @export
 mcmc_plot.mvgam = function(object,
@@ -75,13 +80,21 @@ mcmc_plot.mvgam = function(object,
     # x refers to a data.frame of draws
     draws <- as.array(object, variable = variable, regex = regex,
                       use_alias = use_alias)
-    sel_variables <- dimnames(draws)[[2]]
+    sel_variables <- dimnames(draws)$variable
     if(type %in% c("scatter", "hex") && length(sel_variables) != 2L){
       stop("Exactly 2 parameters must be selected for this type.",
                    "\nParameters selected: ",
                    paste0("'", sel_variables, "'", collapse = ", "),
            call. = FALSE)
     }
+
+    if(type == 'pairs' && length(sel_variables) == 1L){
+      stop("2 or more parameters must be selected for this type.",
+           "\nParameters selected: ",
+           paste0("'", sel_variables, "'", collapse = ", "),
+           call. = FALSE)
+    }
+
     mcmc_args$x <- draws
     }
   }
diff --git a/R/mvgam.R b/R/mvgam.R
index 08ad7f97..4c9d920b 100644
--- a/R/mvgam.R
+++ b/R/mvgam.R
@@ -63,6 +63,7 @@
 #'   \item`student_t()` for real-valued data
 #'   \item`Gamma()` for non-negative real-valued data}
 #'Note that only `nb()` and `poisson()` are available if using `JAGS` as the backend.
+#'Default is `poisson()`.
 #'See \code{\link{mvgam_families}} for more details
 #'@param use_lv \code{logical}. If \code{TRUE}, use dynamic factors to estimate series'
 #'latent trends in a reduced dimension format. Defaults to \code{FALSE}
diff --git a/R/plot.mvgam.R b/R/plot.mvgam.R
index 7e6f913f..3195bbfc 100644
--- a/R/plot.mvgam.R
+++ b/R/plot.mvgam.R
@@ -2,7 +2,6 @@
 #'
 #'This function takes a fitted \code{mvgam} object and produces plots of smooth functions, forecasts, trends and
 #'uncertainty components
-#'
 #'@param x \code{list} object returned from \code{mvgam}. See [mvgam()]
 #'@param type \code{character} specifying which type of plot to return. Options are:
 #'series,
@@ -14,7 +13,7 @@
 #'trend,
 #'uncertainty,
 #'factors
-#'@param residuals \code{logical}. If \code{TRUE} and `type = residuals`, posterior quantiles of partial residuals are added
+#'@param residuals \code{logical}. If \code{TRUE} and `type = 'smooths'`, posterior quantiles of partial residuals are added
 #'to plots of 1-D smooths as a series of ribbon rectangles.
 #'Partial residuals for a smooth term are the median Dunn-Smyth residuals that would be obtained by dropping the term
 #'concerned from the model, while leaving all other estimates fixed (i.e. the
diff --git a/R/plot_mvgam_factors.R b/R/plot_mvgam_factors.R
index baa0061f..be035c72 100644
--- a/R/plot_mvgam_factors.R
+++ b/R/plot_mvgam_factors.R
@@ -3,7 +3,7 @@
 #'This function takes a fitted \code{mvgam} object and returns plots and summary statistics for
 #'the latent dynamic factors
 #'
-#'@param object \code{list} object returned from \code{mvgam}
+#'@inheritParams plot.mvgam
 #'@param plot \code{logical} specifying whether factors should be plotted
 #'@author Nicholas J Clark
 #'@details If the model in \code{object} was estimated using dynamic factors, it is possible that not all factors
diff --git a/R/plot_mvgam_fc.R b/R/plot_mvgam_fc.R
index e8287cdd..45729449 100644
--- a/R/plot_mvgam_fc.R
+++ b/R/plot_mvgam_fc.R
@@ -1,6 +1,6 @@
 #'Plot mvgam posterior predictions for a specified series
 #'@importFrom stats formula terms
-#'@param object \code{list} object returned from \code{mvgam}
+#'@inheritParams plot.mvgam
 #'@param series \code{integer} specifying which series in the set is to be plotted
 #'@param newdata Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
 #'in addition to any other variables included in the linear predictor of the original \code{formula}. If included, the
diff --git a/R/plot_mvgam_pterms.R b/R/plot_mvgam_pterms.R
index b59716df..6feebec6 100644
--- a/R/plot_mvgam_pterms.R
+++ b/R/plot_mvgam_pterms.R
@@ -4,9 +4,7 @@
 #'
 #'@importFrom graphics layout title rug bxp
 #'@importFrom stats coef predict
-#'@param object \code{list} object returned from \code{mvgam}
-#'@param trend_effects logical. If `TRUE` and a `trend_formula` was used in model
-#'fitting, terms from the trend (i.e. process) model will be plotted
+#'@inheritParams plot.mvgam
 #'@details Posterior empirical quantiles of each parametric term's partial effect estimates
 #'(on the link scale) are calculated and visualised as ribbon plots. These effects can
 #'be interpreted as the partial effect that a parametric term contributes when all other
diff --git a/R/plot_mvgam_randomeffects.R b/R/plot_mvgam_randomeffects.R
index 419a4da7..3f05d65f 100644
--- a/R/plot_mvgam_randomeffects.R
+++ b/R/plot_mvgam_randomeffects.R
@@ -3,9 +3,7 @@
 #'This function plots posterior empirical quantiles for random effect smooths (bs = re)
 #'
 #'@importFrom graphics layout title
-#'@param object \code{list} object returned from \code{mvgam}
-#'@param trend_effects logical. If `TRUE` and a `trend_formula` was used in model
-#'fitting, terms from the trend (i.e. process) model will be plotted
+#'@inheritParams plot.mvgam
 #'@details Posterior empirical quantiles of random effect coefficient estimates
 #'(on the link scale) are calculated and visualised as ribbon plots.
 #'Labels for coefficients are taken from the levels of the original factor variable
diff --git a/R/plot_mvgam_resids.R b/R/plot_mvgam_resids.R
index eff551f2..0318da36 100644
--- a/R/plot_mvgam_resids.R
+++ b/R/plot_mvgam_resids.R
@@ -5,7 +5,7 @@
 #'@importFrom graphics layout title
 #'@importFrom stats complete.cases qqnorm qqline acf pacf na.pass
 #'@importFrom mgcv bam
-#'@param object \code{list} object returned from \code{mvgam}
+#'@inheritParams plot.mvgam
 #'@param series \code{integer} specifying which series in the set is to be plotted
 #'@param newdata Optional \code{dataframe} or \code{list} of test data containing at least 'series', 'y', and 'time'
 #'in addition to any other variables included in the linear predictor of \code{formula}. If included, the
diff --git a/R/plot_mvgam_smooth.R b/R/plot_mvgam_smooth.R
index 89827be7..2ce98030 100644
--- a/R/plot_mvgam_smooth.R
+++ b/R/plot_mvgam_smooth.R
@@ -4,9 +4,7 @@
 #'
 #'@importFrom grDevices hcl.colors
 #'@importFrom stats quantile predict
-#'@param object \code{list} object returned from \code{mvgam}
-#'@param trend_effects logical. If `TRUE` and a `trend_formula` was used in model
-#'fitting, terms from the trend (i.e. process) model will be plotted
+#'@inheritParams plot.mvgam
 #'@param series \code{integer} specifying which series in the set is to be plotted
 #'@param smooth either a \code{character} or \code{integer} specifying which smooth term to be plotted
 #'@param residuals \code{logical}. If \code{TRUE} then posterior quantiles of partial residuals are added
diff --git a/R/plot_mvgam_trend.R b/R/plot_mvgam_trend.R
index 0abc4653..5fadbb6b 100644
--- a/R/plot_mvgam_trend.R
+++ b/R/plot_mvgam_trend.R
@@ -1,7 +1,7 @@
 #'Plot mvgam latent trend for a specified series
 #'@importFrom graphics par lines polygon box abline
 #'@importFrom stats sd quantile
-#'@param object \code{list} object returned from \code{mvgam}
+#'@inheritParams plot.mvgam
 #'@param series \code{integer} specifying which series in the set is to be plotted
 #'@param newdata Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
 #'in addition to any other variables included in the linear predictor of the original \code{formula}.
diff --git a/R/plot_mvgam_uncertainty.R b/R/plot_mvgam_uncertainty.R
index 693cfaca..2a095449 100644
--- a/R/plot_mvgam_uncertainty.R
+++ b/R/plot_mvgam_uncertainty.R
@@ -1,7 +1,7 @@
 #'Plot mvgam forecast uncertainty contributions for a specified series
 #'@importFrom graphics legend
 #'@importFrom stats predict
-#'@param object \code{list} object returned from \code{mvgam}
+#'@inheritParams plot.mvgam
 #'@param series \code{integer} specifying which series in the set is to be plotted
 #'@param newdata A \code{dataframe} or \code{list} containing at least 'series' and 'time' for the forecast horizon, in
 #'addition to any other variables included in the linear predictor of \code{formula}
diff --git a/R/ppc.mvgam.R b/R/ppc.mvgam.R
index d1cb954d..47ddf108 100644
--- a/R/ppc.mvgam.R
+++ b/R/ppc.mvgam.R
@@ -39,7 +39,21 @@
 #'series (for \code{type == 'hist'}), kernel density or empirical CDF estimates for
 #'posterior predictions (for \code{type == 'density'} or \code{type == 'cdf'}) or a Probability
 #'Integral Transform histogram (for \code{type == 'pit'}).
+#' @examples
+#' \dontrun{
+#' # Simulate some smooth effects and fit a model
+#' set.seed(0)
+#' dat <- mgcv::gamSim(1, n = 200, scale = 2)
+#' dat$time <- 1:NROW(dat)
+#' mod <- mvgam(y ~ s(x0) + s(x1) + s(x2) + s(x3),
+#'             data = dat,
+#'             family = gaussian())
 #'
+#' # Posterior checks
+#' ppc(mod, type = 'hist')
+#' ppc(mod, type = 'density')
+#' ppc(mod, type = 'cdf')
+#' }
 #'@export
 ppc <- function(object, ...){
   UseMethod("ppc", object)
diff --git a/R/predict.mvgam.R b/R/predict.mvgam.R
index 2313b769..1d88c064 100644
--- a/R/predict.mvgam.R
+++ b/R/predict.mvgam.R
@@ -1,6 +1,6 @@
 #'Predict from the GAM component of an mvgam model
 #'@importFrom stats predict
-#'@param object Object of class `mvgam`
+#'@param object \code{list} object returned from \code{mvgam}. See [mvgam()]
 #'@param newdata Optional \code{dataframe} or \code{list} of test data containing the
 #'variables included in the linear predictor of \code{formula}. If not supplied,
 #'predictions are generated for the original observations used for the model fit.
diff --git a/R/score.mvgam_forecast.R b/R/score.mvgam_forecast.R
index 3752b094..081e6c65 100644
--- a/R/score.mvgam_forecast.R
+++ b/R/score.mvgam_forecast.R
@@ -32,21 +32,23 @@
 #'will only contain the linear predictors
 #'@examples
 #'\dontrun{
-#'#Simulate observations for three count-valued time series
+#'# Simulate observations for three count-valued time series
 #'data <- sim_mvgam()
-#'#Fit a dynamic model using 'newdata' to automatically produce forecasts
+#'# Fit a dynamic model using 'newdata' to automatically produce forecasts
 #'mod <- mvgam(y ~ 1,
 #'             trend_model = 'RW',
 #'             data = data$data_train,
 #'             newdata = data$data_test)
 #'
-#'#Extract forecasts into a 'mvgam_forecast' object
+#'# Extract forecasts into a 'mvgam_forecast' object
 #'fc <- forecast(mod)
 #'
-#'#Score forecasts
-#'score(fc, score = 'drps')
+#'# Compute Discrete Rank Probability Scores and 0.90 interval coverages
+#'fc_scores <- score(fc, score = 'drps')
+#'str(fc_scores)
 #'}
 #'@method score mvgam_forecast
+#'@seealso \code{\link{forecast.mvgam}}
 #'@export
 score.mvgam_forecast = function(object, score = 'crps',
                                 log = FALSE, weights,
diff --git a/R/update.mvgam.R b/R/update.mvgam.R
index f8ce3e2c..b61b003f 100644
--- a/R/update.mvgam.R
+++ b/R/update.mvgam.R
@@ -4,56 +4,11 @@
 #'@name update.mvgam
 #'@importFrom mgcv nb betar
 #'@importFrom rlang missing_arg
-#'@param object A fitted `mvgam` model
+#'@inheritParams mvgam
+#'@param object \code{list} object returned from \code{mvgam}. See [mvgam()]
 #'@param formula Optional new `formula` object. Note, `mvgam` currently does not support dynamic formula
 #'updates such as removal of specific terms with `- term`. When updating, the entire formula needs
 #'to be supplied
-#'@param trend_formula An optional \code{character} string specifying the GAM process model formula. If
-#'supplied, a linear predictor will be modelled for the latent trends to capture process model evolution
-#'separately from the observation model. Should not have a response variable specified on the left-hand side
-#'of the formula (i.e. a valid option would be `~ season + s(year)`). This feature is experimental, and is only
-#'currently available for Random Walk trend models.
-#'@param data A \code{dataframe} or \code{list} containing the model response variable and covariates
-#'required by the GAM \code{formula}. Should include columns:
-#''series' (character or factor index of the series IDs)
-#''time' (numeric index of the time point for each observation).
-#'Any other variables to be included in the linear predictor of \code{formula} must also be present
-#'@param newdata Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
-#'in addition to any other variables included in the linear predictor of \code{formula}. If included, the
-#'observations in variable \code{y} will be set to \code{NA} when fitting the model so that posterior
-#'simulations can be obtained
-#'@param trend_model \code{character} specifying the time series dynamics for the latent trend. Options are:
-#'\itemize{
-#'   \item `None` (no latent trend component; i.e. the GAM component is all that contributes to the linear predictor,
-#'and the observation process is the only source of error; similarly to what is estimated by \code{\link[mgcv]{gam}})
-#'   \item `RW` (random walk with possible drift)
-#'   \item `AR1` (with possible drift)
-#'   \item `AR2` (with possible drift)
-#'   \item `AR3` (with possible drift)
-#'   \item `VAR1` (with possible drift; only available in \code{Stan})
-#'   \item `GP` (Gaussian Process with squared exponential kernel;
-#'only available in \code{Stan})}
-#'@param trend_map Optional `data.frame` specifying which series should depend on which latent
-#'trends. Useful for allowing multiple series to depend on the same latent trend process, but with
-#'different observation processes. If supplied, a latent factor model is set up by setting
-#'`use_lv = TRUE` and using the mapping to set up the shared trends. Needs to have column names
-#'`series` and `trend`, with integer values in the `trend` column to state which trend each series
-#'should depend on. The `series` column should have a single unique entry for each series in the
-#'data (names should perfectly match factor levels of the `series` variable in `data`). See examples
-#'in \code{\link{mvgam}} for details
-#'@param family \code{family} specifying the exponential observation family for the series. Currently supported
-#'families are: `nb()`, `poisson()`, `tweedie()`, `gaussian()`, `betar()`, `lognormal()`, `student_t()` and `Gamma()`
-#'@param use_lv \code{logical}. If \code{TRUE}, use dynamic factors to estimate series'
-#'latent trends in a reduced dimension format. If \code{FALSE}, estimate independent latent trends for each series
-#'@param n_lv \code{integer} the number of latent dynamic factors to use if \code{use_lv == TRUE}.
-#'Cannot be \code{>n_series}. Defaults arbitrarily to \code{min(2, floor(n_series / 2))}
-#'@param priors An optional \code{data.frame} with prior
-#'definitions. See \code{\link{get_mvgam_priors}} and
-#'[mvgam] for more information on changing default prior distributions
-#'@param lfo Logical indicating whether this is part of a call to [lfo_cv.mvgam]. Returns a
-#'lighter version of the model with no residuals and fewer monitored parameters to speed up
-#'post-processing. But other downstream functions will not work properly, so users should always
-#'leave this set as `FALSE`
 #'@param ... Other arguments to be passed to \code{\link{mvgam}}
 #' @examples
 #' \dontrun{
diff --git a/man/conditional_effects.mvgam.Rd b/man/conditional_effects.mvgam.Rd
index 9b491b48..2f4da81b 100644
--- a/man/conditional_effects.mvgam.Rd
+++ b/man/conditional_effects.mvgam.Rd
@@ -101,6 +101,9 @@ conditional_effects(mod)
 conditional_effects(mod, conf_level = 0.5, type = 'link')
 }
 }
+\seealso{
+\code{\link[marginaleffects]{plot_predictions}}, \code{\link[marginaleffects]{plot_slopes}}
+}
 \author{
 Nicholas J Clark
 }
diff --git a/man/forecast.mvgam.Rd b/man/forecast.mvgam.Rd
index 6e209f1d..3051e2d7 100644
--- a/man/forecast.mvgam.Rd
+++ b/man/forecast.mvgam.Rd
@@ -41,12 +41,12 @@ as it saves recomputing the full set of trends for each series individually}
 
 \item{n_cores}{\code{integer} specifying number of cores for generating forecasts in parallel}
 
-\item{type}{When this has the value \code{link} the linear predictor is calculated on the link scale.
+\item{type}{When this has the value \code{link} (default) the linear predictor is calculated on the link scale.
 If \code{expected} is used, predictions reflect the expectation of the response (the mean)
-but ignore uncertainty in the observation process. When \code{response} (default) is used,
+but ignore uncertainty in the observation process. When \code{response} is used,
 the predictions take uncertainty in the observation process into account to return
-predictions on the outcome scale. When \code{trend} is used, only the forecast distribution for the
-latent trend is returned}
+predictions on the outcome scale. When \code{variance} is used, the variance of the response
+with respect to the mean (mean-variance relationship) is returned}
 }
 \value{
 An object of class \code{mvgam_forecast} containing hindcast and forecast distributions.
@@ -87,3 +87,6 @@ plot(fc, series = 3)
 
 }
 }
+\seealso{
+\code{\link{hindcast}}, \code{\link{score}}
+}
diff --git a/man/formula.mvgam.Rd b/man/formula.mvgam.Rd
index cc8d2cf2..497051fc 100644
--- a/man/formula.mvgam.Rd
+++ b/man/formula.mvgam.Rd
@@ -3,7 +3,7 @@
 \name{formula.mvgam}
 \alias{formula.mvgam}
 \alias{formula.mvgam_prefit}
-\title{Extract model.frame from a fitted mvgam object}
+\title{Extract formulae from mvgam objects}
 \usage{
 \method{formula}{mvgam}(x, trend_effects = FALSE, ...)
 
@@ -12,17 +12,17 @@
 \arguments{
 \item{x}{\code{mvgam} or \code{mvgam_prefit} object}
 
-\item{trend_effects}{\code{logical}, return the model.frame from the
+\item{trend_effects}{\code{logical}, return the formula from the
 observation model (if \code{FALSE}) or from the underlying process
 model (if\code{TRUE})}
 
 \item{...}{Ignored}
 }
 \value{
-A \code{matrix} containing the fitted model frame
+A \code{formula} object
 }
 \description{
-Extract model.frame from a fitted mvgam object
+Extract formulae from mvgam objects
 }
 \author{
 Nicholas J Clark
diff --git a/man/get_mvgam_priors.Rd b/man/get_mvgam_priors.Rd
index 0a30f373..c261a995 100644
--- a/man/get_mvgam_priors.Rd
+++ b/man/get_mvgam_priors.Rd
@@ -54,6 +54,7 @@ families are:
 \item\code{student_t()} for real-valued data
 \item\code{Gamma()} for non-negative real-valued data}
 Note that only \code{nb()} and \code{poisson()} are available if using \code{JAGS} as the backend.
+Default is \code{poisson()}.
 See \code{\link{mvgam_families}} for more details}
 
 \item{use_lv}{\code{logical}. If \code{TRUE}, use dynamic factors to estimate series'
@@ -62,13 +63,11 @@ latent trends in a reduced dimension format. Defaults to \code{FALSE}}
 \item{n_lv}{\code{integer} the number of latent dynamic factors to use if \code{use_lv == TRUE}.
 Cannot be \code{>n_series}. Defaults arbitrarily to \code{min(2, floor(n_series / 2))}}
 
-\item{use_stan}{Logical. If \code{TRUE} and if \code{rstan} is installed, the model will be compiled and sampled using
-the Hamiltonian Monte Carlo with a call to \code{\link[cmdstanr]{cmdstan_model}} or, if \code{cmdstanr} is not available,
-a call to \code{\link[rstan]{stan}}. Note that this functionality is still in development and
-not all options that are available in \code{JAGS} can be used, including: no option for a Tweedie family and no option for
-dynamic factor trends. However, as \code{Stan} can estimate Hilbert base approximate Gaussian Processes, which
-are much more computationally tractable than full GPs for time series with \verb{>100} observations, estimation
-in \code{Stan} can support latent GP trends while estimation in \code{JAGS} cannot}
+\item{use_stan}{Logical. If \code{TRUE}, the model will be compiled and sampled using
+Hamiltonian Monte Carlo with a call to \code{\link[cmdstanr]{cmdstan_model}} or
+a call to \code{\link[rstan]{stan}}. Note that
+there are many more options when using \code{Stan} vs \code{JAGS} (the only "advantage" of \code{JAGS} is the ability
+to use a Tweedie family).}
 
 \item{trend_model}{\code{character} or  \code{function} specifying the time series dynamics for the latent trend. Options are:
 \itemize{
@@ -241,7 +240,7 @@ code(mod2)
 
 }
 \seealso{
-\code{\link{mvgam}}
+\code{\link{mvgam}} \code{\link[brms]{prior}}
 }
 \author{
 Nicholas J Clark
diff --git a/man/hindcast.mvgam.Rd b/man/hindcast.mvgam.Rd
index 10e551eb..588c00d5 100644
--- a/man/hindcast.mvgam.Rd
+++ b/man/hindcast.mvgam.Rd
@@ -19,16 +19,17 @@ or the character string \code{'all'}, specifying that all series should be forec
 if the fitted model contained multivariate trends (either as a dynamic factor or \code{VAR} process),
 as it saves recomputing the full set of trends for each series individually}
 
-\item{type}{When this has the value \code{link} the linear predictor is calculated on the link scale.
+\item{type}{When this has the value \code{link} (default) the linear predictor is calculated on the link scale.
 If \code{expected} is used, predictions reflect the expectation of the response (the mean)
-but ignore uncertainty in the observation process. When \code{response} (default) is used,
+but ignore uncertainty in the observation process. When \code{response} is used,
 the predictions take uncertainty in the observation process into account to return
-predictions on the outcome scale. When \code{trend} is used, only the hindcast distribution for the
-latent trend is returned}
+predictions on the outcome scale. When \code{variance} is used, the variance of the response
+with respect to the mean (mean-variance relationship) is returned}
 }
 \value{
 An object of class \code{mvgam_forecast} containing hindcast distributions.
 See \code{\link{mvgam_forecast-class}} for details.
+#'@seealso \code{\link{forecast.mvgam}}
 }
 \description{
 Extract hindcasts for a fitted \code{mvgam} object
diff --git a/man/index-mvgam.Rd b/man/index-mvgam.Rd
index 6d38c66e..1f5d8473 100644
--- a/man/index-mvgam.Rd
+++ b/man/index-mvgam.Rd
@@ -15,7 +15,7 @@
 \method{variables}{mvgam}(x, ...)
 }
 \arguments{
-\item{x}{\code{list} object of class \code{mvgam}}
+\item{x}{\code{list} object returned from \code{mvgam}. See \code{\link[=mvgam]{mvgam()}}}
 
 \item{...}{Arguments passed to individual methods (if applicable).}
 }
diff --git a/man/mcmc_plot.mvgam.Rd b/man/mcmc_plot.mvgam.Rd
index 42196808..7e178f76 100644
--- a/man/mcmc_plot.mvgam.Rd
+++ b/man/mcmc_plot.mvgam.Rd
@@ -19,8 +19,10 @@
 \item{type}{The type of the plot.
 Supported types are (as names) \code{hist}, \code{dens},
 \code{hist_by_chain}, \code{dens_overlay},
-\code{violin}, \code{intervals}, \code{areas}, \code{acf},
-\code{acf_bar},\code{trace}, \code{trace_highlight}, \code{scatter},
+\code{violin}, \code{intervals}, \code{areas},
+\code{areas_ridges}, \code{combo}, \code{acf},
+\code{acf_bar}, \code{trace}, \code{trace_highlight},
+\code{scatter}, \code{violin},
 \code{rhat}, \code{rhat_hist}, \code{neff}, \code{neff_hist}
 and \code{nuts_energy}.
 For an overview on the various plot types see
@@ -57,5 +59,11 @@ mod <- mvgam(y ~ s(season, bs = 'cc'),
             data = simdat$data_train)
 mcmc_plot(mod)
 mcmc_plot(mod, type = 'neff_hist')
+mcmc_plot(mod, variable = 'betas', type = 'areas')
+mcmc_plot(mod, variable = 'trend_params', type = 'combo')
 }
 }
+\seealso{
+\code{\link{mvgam_draws}} for an overview of some of the shortcut strings
+that can be used for argument \code{variable}
+}
diff --git a/man/mvgam.Rd b/man/mvgam.Rd
index 94a26ff6..3d4da0e7 100644
--- a/man/mvgam.Rd
+++ b/man/mvgam.Rd
@@ -106,6 +106,7 @@ families are:
 \item\code{student_t()} for real-valued data
 \item\code{Gamma()} for non-negative real-valued data}
 Note that only \code{nb()} and \code{poisson()} are available if using \code{JAGS} as the backend.
+Default is \code{poisson()}.
 See \code{\link{mvgam_families}} for more details}
 
 \item{use_lv}{\code{logical}. If \code{TRUE}, use dynamic factors to estimate series'
diff --git a/man/plot.mvgam.Rd b/man/plot.mvgam.Rd
index d0e0d2be..6c34742d 100644
--- a/man/plot.mvgam.Rd
+++ b/man/plot.mvgam.Rd
@@ -32,7 +32,7 @@ factors}
 \item{series}{\code{integer} specifying which series in the set is to be plotted. This is ignored
 if \code{type == 're'}}
 
-\item{residuals}{\code{logical}. If \code{TRUE} and \code{type = residuals}, posterior quantiles of partial residuals are added
+\item{residuals}{\code{logical}. If \code{TRUE} and \code{type = 'smooths'}, posterior quantiles of partial residuals are added
 to plots of 1-D smooths as a series of ribbon rectangles.
 Partial residuals for a smooth term are the median Dunn-Smyth residuals that would be obtained by dropping the term
 concerned from the model, while leaving all other estimates fixed (i.e. the
diff --git a/man/plot_mvgam_factors.Rd b/man/plot_mvgam_factors.Rd
index 859211de..d9f81f1e 100644
--- a/man/plot_mvgam_factors.Rd
+++ b/man/plot_mvgam_factors.Rd
@@ -7,8 +7,6 @@
 plot_mvgam_factors(object, plot = TRUE)
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{plot}{\code{logical} specifying whether factors should be plotted}
 }
 \value{
diff --git a/man/plot_mvgam_forecasts.Rd b/man/plot_mvgam_forecasts.Rd
index e389682c..81bfe154 100644
--- a/man/plot_mvgam_forecasts.Rd
+++ b/man/plot_mvgam_forecasts.Rd
@@ -37,8 +37,6 @@ plot_mvgam_fc(
 )
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{series}{\code{integer} specifying which series in the set is to be plotted}
 
 \item{newdata}{Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
diff --git a/man/plot_mvgam_pterms.Rd b/man/plot_mvgam_pterms.Rd
index 73ba2f9a..1799f82d 100644
--- a/man/plot_mvgam_pterms.Rd
+++ b/man/plot_mvgam_pterms.Rd
@@ -7,8 +7,6 @@
 plot_mvgam_pterms(object, trend_effects = FALSE)
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{trend_effects}{logical. If \code{TRUE} and a \code{trend_formula} was used in model
 fitting, terms from the trend (i.e. process) model will be plotted}
 }
diff --git a/man/plot_mvgam_randomeffects.Rd b/man/plot_mvgam_randomeffects.Rd
index 9fc4dfc1..9210005f 100644
--- a/man/plot_mvgam_randomeffects.Rd
+++ b/man/plot_mvgam_randomeffects.Rd
@@ -7,8 +7,6 @@
 plot_mvgam_randomeffects(object, trend_effects = FALSE)
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{trend_effects}{logical. If \code{TRUE} and a \code{trend_formula} was used in model
 fitting, terms from the trend (i.e. process) model will be plotted}
 }
diff --git a/man/plot_mvgam_resids.Rd b/man/plot_mvgam_resids.Rd
index 0d2d2d83..22503ad3 100644
--- a/man/plot_mvgam_resids.Rd
+++ b/man/plot_mvgam_resids.Rd
@@ -7,8 +7,6 @@
 plot_mvgam_resids(object, series = 1, newdata, data_test)
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{series}{\code{integer} specifying which series in the set is to be plotted}
 
 \item{newdata}{Optional \code{dataframe} or \code{list} of test data containing at least 'series', 'y', and 'time'
diff --git a/man/plot_mvgam_smooth.Rd b/man/plot_mvgam_smooth.Rd
index 65900446..6bf28bf6 100644
--- a/man/plot_mvgam_smooth.Rd
+++ b/man/plot_mvgam_smooth.Rd
@@ -18,8 +18,6 @@ plot_mvgam_smooth(
 )
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{trend_effects}{logical. If \code{TRUE} and a \code{trend_formula} was used in model
 fitting, terms from the trend (i.e. process) model will be plotted}
 
diff --git a/man/plot_mvgam_trend.Rd b/man/plot_mvgam_trend.Rd
index 36b9012e..900b09ce 100644
--- a/man/plot_mvgam_trend.Rd
+++ b/man/plot_mvgam_trend.Rd
@@ -20,8 +20,6 @@ plot_mvgam_trend(
 )
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{series}{\code{integer} specifying which series in the set is to be plotted}
 
 \item{newdata}{Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
diff --git a/man/plot_mvgam_uncertainty.Rd b/man/plot_mvgam_uncertainty.Rd
index d7824ba6..8e217ee7 100644
--- a/man/plot_mvgam_uncertainty.Rd
+++ b/man/plot_mvgam_uncertainty.Rd
@@ -14,8 +14,6 @@ plot_mvgam_uncertainty(
 )
 }
 \arguments{
-\item{object}{\code{list} object returned from \code{mvgam}}
-
 \item{series}{\code{integer} specifying which series in the set is to be plotted}
 
 \item{newdata}{A \code{dataframe} or \code{list} containing at least 'series' and 'time' for the forecast horizon, in
diff --git a/man/posterior_epred.mvgam.Rd b/man/posterior_epred.mvgam.Rd
index c93368c6..1911f035 100644
--- a/man/posterior_epred.mvgam.Rd
+++ b/man/posterior_epred.mvgam.Rd
@@ -8,7 +8,7 @@
 \method{posterior_epred}{mvgam}(object, newdata, data_test, process_error = TRUE, ...)
 }
 \arguments{
-\item{object}{Object of class \code{mvgam}}
+\item{object}{\code{list} object returned from \code{mvgam}. See \code{\link[=mvgam]{mvgam()}}}
 
 \item{newdata}{Optional \code{dataframe} or \code{list} of test data containing the
 variables included in the linear predictor of \code{formula}. If not supplied,
diff --git a/man/posterior_linpred.mvgam.Rd b/man/posterior_linpred.mvgam.Rd
index 672f734b..dd125dcb 100644
--- a/man/posterior_linpred.mvgam.Rd
+++ b/man/posterior_linpred.mvgam.Rd
@@ -14,7 +14,7 @@
 )
 }
 \arguments{
-\item{object}{Object of class \code{mvgam}}
+\item{object}{\code{list} object returned from \code{mvgam}. See \code{\link[=mvgam]{mvgam()}}}
 
 \item{transform}{Logical; if \code{FALSE}
 (the default), draws of the linear predictor are returned.
diff --git a/man/posterior_predict.mvgam.Rd b/man/posterior_predict.mvgam.Rd
index b765b6fc..0c437971 100644
--- a/man/posterior_predict.mvgam.Rd
+++ b/man/posterior_predict.mvgam.Rd
@@ -7,7 +7,7 @@
 \method{posterior_predict}{mvgam}(object, newdata, data_test, process_error = TRUE, ...)
 }
 \arguments{
-\item{object}{Object of class \code{mvgam}}
+\item{object}{\code{list} object returned from \code{mvgam}. See \code{\link[=mvgam]{mvgam()}}}
 
 \item{newdata}{Optional \code{dataframe} or \code{list} of test data containing the
 variables included in the linear predictor of \code{formula}. If not supplied,
diff --git a/man/ppc.mvgam.Rd b/man/ppc.mvgam.Rd
index 139a8d84..ca29054c 100644
--- a/man/ppc.mvgam.Rd
+++ b/man/ppc.mvgam.Rd
@@ -74,3 +74,19 @@ can also be compared to out of sample observations as long as these observations
 'data_test' in the original model fit and supplied here. Rootograms are currently only plotted using the
 'hanging' style
 }
+\examples{
+\dontrun{
+# Simulate some smooth effects and fit a model
+set.seed(0)
+dat <- mgcv::gamSim(1, n = 200, scale = 2)
+dat$time <- 1:NROW(dat)
+mod <- mvgam(y ~ s(x0) + s(x1) + s(x2) + s(x3),
+            data = dat,
+            family = gaussian())
+
+# Posterior checks
+ppc(mod, type = 'hist')
+ppc(mod, type = 'density')
+ppc(mod, type = 'cdf')
+}
+}
diff --git a/man/predict.mvgam.Rd b/man/predict.mvgam.Rd
index 45f7535b..6a5265c0 100644
--- a/man/predict.mvgam.Rd
+++ b/man/predict.mvgam.Rd
@@ -7,7 +7,7 @@
 \method{predict}{mvgam}(object, newdata, data_test, type = "link", process_error = TRUE, ...)
 }
 \arguments{
-\item{object}{Object of class \code{mvgam}}
+\item{object}{\code{list} object returned from \code{mvgam}. See \code{\link[=mvgam]{mvgam()}}}
 
 \item{newdata}{Optional \code{dataframe} or \code{list} of test data containing the
 variables included in the linear predictor of \code{formula}. If not supplied,
diff --git a/man/score.mvgam_forecast.Rd b/man/score.mvgam_forecast.Rd
index a3a22cbb..8dedb3fd 100644
--- a/man/score.mvgam_forecast.Rd
+++ b/man/score.mvgam_forecast.Rd
@@ -59,18 +59,22 @@ Compute probabilistic forecast scores for mvgam objects
 }
 \examples{
 \dontrun{
-#Simulate observations for three count-valued time series
+# Simulate observations for three count-valued time series
 data <- sim_mvgam()
-#Fit a dynamic model using 'newdata' to automatically produce forecasts
+# Fit a dynamic model using 'newdata' to automatically produce forecasts
 mod <- mvgam(y ~ 1,
             trend_model = 'RW',
             data = data$data_train,
             newdata = data$data_test)
 
-#Extract forecasts into a 'mvgam_forecast' object
+# Extract forecasts into a 'mvgam_forecast' object
 fc <- forecast(mod)
 
-#Score forecasts
-score(fc, score = 'drps')
+# Compute Discrete Rank Probability Scores and 0.90 interval coverages
+fc_scores <- score(fc, score = 'drps')
+str(fc_scores)
 }
 }
+\seealso{
+\code{\link{forecast.mvgam}}
+}
diff --git a/man/update.mvgam.Rd b/man/update.mvgam.Rd
index 85dbed0f..532918da 100644
--- a/man/update.mvgam.Rd
+++ b/man/update.mvgam.Rd
@@ -21,7 +21,7 @@
 )
 }
 \arguments{
-\item{object}{A fitted \code{mvgam} model}
+\item{object}{\code{list} object returned from \code{mvgam}. See \code{\link[=mvgam]{mvgam()}}}
 
 \item{formula}{Optional new \code{formula} object. Note, \code{mvgam} currently does not support dynamic formula
 updates such as removal of specific terms with \code{- term}. When updating, the entire formula needs
@@ -30,31 +30,39 @@ to be supplied}
 \item{trend_formula}{An optional \code{character} string specifying the GAM process model formula. If
 supplied, a linear predictor will be modelled for the latent trends to capture process model evolution
 separately from the observation model. Should not have a response variable specified on the left-hand side
-of the formula (i.e. a valid option would be \code{~ season + s(year)}). This feature is experimental, and is only
-currently available for Random Walk trend models.}
+of the formula (i.e. a valid option would be \code{~ season + s(year)}). Also note that you should not use
+the identifier \code{series} in this formula to specify effects that vary across time series. Instead you should use
+\code{trend}. This will ensure that models in which a \code{trend_map} is supplied will still work consistently
+(i.e. by allowing effects to vary across process models, even when some time series share the same underlying
+process model). This feature is experimental, and is only currently available for Random Walk and AR trend models.}
 
 \item{data}{A \code{dataframe} or \code{list} containing the model response variable and covariates
 required by the GAM \code{formula}. Should include columns:
-'series' (character or factor index of the series IDs)
-'time' (numeric index of the time point for each observation).
+\code{series} (a \code{factor} index of the series IDs;the number of levels should be identical
+to the number of unique series labels (i.e. \code{n_series = length(levels(data$series))}))
+\code{time} (\code{numeric} or \code{integer} index of the time point for each observation).
 Any other variables to be included in the linear predictor of \code{formula} must also be present}
 
-\item{newdata}{Optional \code{dataframe} or \code{list} of test data containing at least 'series' and 'time'
+\item{newdata}{Optional \code{dataframe} or \code{list} of test data containing at least \code{series} and \code{time}
 in addition to any other variables included in the linear predictor of \code{formula}. If included, the
 observations in variable \code{y} will be set to \code{NA} when fitting the model so that posterior
 simulations can be obtained}
 
-\item{trend_model}{\code{character} specifying the time series dynamics for the latent trend. Options are:
+\item{trend_model}{\code{character} or  \code{function} specifying the time series dynamics for the latent trend. Options are:
 \itemize{
 \item \code{None} (no latent trend component; i.e. the GAM component is all that contributes to the linear predictor,
 and the observation process is the only source of error; similarly to what is estimated by \code{\link[mgcv]{gam}})
-\item \code{RW} (random walk with possible drift)
-\item \code{AR1} (with possible drift)
-\item \code{AR2} (with possible drift)
-\item \code{AR3} (with possible drift)
-\item \code{VAR1} (with possible drift; only available in \code{Stan})
-\item \code{GP} (Gaussian Process with squared exponential kernel;
-only available in \code{Stan})}}
+\item \code{'RW'} or \code{RW()}
+\item \code{'AR1'} or \code{AR(p = 1)}
+\item \code{'AR2'} or \code{AR(p = 2)}
+\item \code{'AR3'} or \code{AR(p = 3)}
+\item \code{'VAR1'}  or \code{VAR()}(only available in \code{Stan})
+\item \code{'GP'} (Gaussian Process with squared exponential kernel;
+only available in \code{Stan})}
+
+For all types apart from \code{'GP'}, moving average and/or correlated
+process error terms can also be estimated (for example, \code{RW(cor = TRUE)} will set up a
+multivariate Random Walk if \code{n_series > 1}). See \link{mvgam_trends} for more details}
 
 \item{trend_map}{Optional \code{data.frame} specifying which series should depend on which latent
 trends. Useful for allowing multiple series to depend on the same latent trend process, but with
@@ -63,20 +71,32 @@ different observation processes. If supplied, a latent factor model is set up by
 \code{series} and \code{trend}, with integer values in the \code{trend} column to state which trend each series
 should depend on. The \code{series} column should have a single unique entry for each series in the
 data (names should perfectly match factor levels of the \code{series} variable in \code{data}). See examples
-in \code{\link{mvgam}} for details}
+for details}
 
 \item{use_lv}{\code{logical}. If \code{TRUE}, use dynamic factors to estimate series'
-latent trends in a reduced dimension format. If \code{FALSE}, estimate independent latent trends for each series}
+latent trends in a reduced dimension format. Defaults to \code{FALSE}}
 
 \item{n_lv}{\code{integer} the number of latent dynamic factors to use if \code{use_lv == TRUE}.
 Cannot be \code{>n_series}. Defaults arbitrarily to \code{min(2, floor(n_series / 2))}}
 
 \item{family}{\code{family} specifying the exponential observation family for the series. Currently supported
-families are: \code{nb()}, \code{poisson()}, \code{tweedie()}, \code{gaussian()}, \code{betar()}, \code{lognormal()}, \code{student_t()} and \code{Gamma()}}
+families are:
+\itemize{
+\item\code{nb()} for count data
+\item\code{poisson()} for count data
+\item\code{gaussian()} for real-valued data
+\item\code{betar()} for proportional data on \verb{(0,1)}
+\item\code{lognormal()} for non-negative real-valued data
+\item\code{student_t()} for real-valued data
+\item\code{Gamma()} for non-negative real-valued data}
+Note that only \code{nb()} and \code{poisson()} are available if using \code{JAGS} as the backend.
+Default is \code{poisson()}.
+See \code{\link{mvgam_families}} for more details}
 
 \item{priors}{An optional \code{data.frame} with prior
-definitions. See \code{\link{get_mvgam_priors}} and
-\link{mvgam} for more information on changing default prior distributions}
+definitions (in JAGS or Stan syntax). if using Stan, this can also be an object of
+class \code{brmsprior} (see. \code{\link[brms]{prior}} for details). See \link{get_mvgam_priors} and
+'Details' for more information on changing default prior distributions}
 
 \item{lfo}{Logical indicating whether this is part of a call to \link{lfo_cv.mvgam}. Returns a
 lighter version of the model with no residuals and fewer monitored parameters to speed up
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