minor cleaning

DESCRIPTION

@@ -1,13 +1,13 @@
 Package: mvgam
 Title: Multivariate (Dynamic) Generalized Additive Models
 Version: 1.1.0
-Date: 2024-03-26
+Date: 2024-05-01
 Authors@R:
     person("Nicholas J", "Clark", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-7131-3301"))
-Description: Fit Bayesian Dynamic Generalised Additive Models to sets of time series. 
+Description: Fit Bayesian Dynamic Generalized Additive Models to sets of time series. 
              The primary purpose of the package is to build dynamic nonlinear models that incorporate 
-             the flexibility of GAMs in observation and process components of State-Space models. 
+             semiparametric effects in observation and process components of State-Space models. 
              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>.

R/globals.R

@@ -17,4 +17,4 @@ utils::globalVariables(c("y", "year", "smooth_vals", "smooth_num",
                          "name", "needed", "nmix_trendmap", "orig_formula",
                          "trial", "use_var1", "use_var1cor", "xcols_drop",
                          "time_lag", "dis_time", "maxt", "orig_rows",
-                         "matches", "time."))
+                         "matches", "time.", "file_name"))

R/posterior_epred.mvgam.R

@@ -265,7 +265,10 @@ posterior_predict.mvgam = function(object,
 #' simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
 #' mod <- mvgam(y ~ s(season, bs = 'cc'),
 #'             trend_model = 'AR1',
-#'             data = simdat$data_train)
+#'             data = simdat$data_train,
+#'             chains = 2,
+#'             burnin = 300,
+#'             samples = 300)
 #'
 #'# Extract fitted values (posterior expectations)
 #'expectations <- fitted(mod)

cran-comments.md

@@ -4,9 +4,10 @@
 * DESCRIPTION has been shortened appropriately
 * Missing Rd tags have been added for all exported functions
 * Examples that previously used non-exported functions have been fixed
-* \dontrun has been replaced with \donttest throughout, but note that many of these examples take some time to run due to the need for Stan models to compile. Using `--run-donttest`, the examples take between 28 and 40 minutes, depending on the test environment. A very similar R package that is on CRAN ('brms') sticks to the \dontrun convention because of this 
+* `\dontrun` has been replaced with `\donttest` throughout, but note that many of these examples take some time to run due to the need for 'Stan' models to compile. Using `--run-donttest`, the examples take between 38 and 50 minutes, depending on the test environment. A very similar R package that is on CRAN ('brms') sticks to the `\dontrun` convention because of this 
 * `cat()` has been replaced with `message()` throughout
 * `onexit()` has been used as suggested to ensure the user's `par` is not changed
+
 ## Test environments
 * Windows install: R 4.3.1
 * win-builder: R-devel
@@ -16,6 +17,6 @@
 * macOS-latest: R-release
 
 ## R CMD check results
-* There were no ERRORs or WARNINGs. There were 2 NOTEs due to listing `cmdstanr` in Suggests. This package is not a dependency but provides an additional backend option for users to select when fitting 'Stan' models, if they wish. A similar package that has been available on CRAN for quite some time ('brms') uses the same convention.
+* There were no ERRORs or WARNINGs. There were 2 NOTEs due to listing 'cmdstanr' in Suggests. This package is not a dependency but provides an additional backend option for users to select when fitting 'Stan' models, if they wish. A similar package that has been available on CRAN for quite some time ('brms') uses the same convention.
 
 Maintainer: 'Nicholas J Clark <[email protected]>'