diff --git a/R/forecast.mvgam.R b/R/forecast.mvgam.R
index 73cd5da4..cd3793ee 100644
--- a/R/forecast.mvgam.R
+++ b/R/forecast.mvgam.R
@@ -861,21 +861,26 @@ forecast_draws = function(object,
                             envir = environment())
 
     # Grab internal functions to export to each worker
-    funs_list <- c('extract_general_trend_pars',
-                   'linkfun',
-                   'forecast_trend',
-                   'extract_series_trend_pars',
-                   'mvgam_predict',
-                   'prep_varma_params',
-                   'sim_varma',
-                   'validate_equaldims',
-                   'varma_recursC',
-                   'log_sum_exp')
-    attr(funs_list, 'envir') <- as.environment(asNamespace("mvgam"))
-    attr(funs_list, 'mode') <- 'function'
+    # funs_list <- c('extract_general_trend_pars',
+    #                'linkfun',
+    #                'forecast_trend',
+    #                'extract_series_trend_pars',
+    #                'mvgam_predict',
+    #                'prep_varma_params',
+    #                'sim_varma',
+    #                'validate_equaldims',
+    #                'varma_recursC',
+    #                'log_sum_exp')
+    # attr(funs_list, 'envir') <- as.environment(asNamespace("mvgam"))
+    # attr(funs_list, 'mode') <- 'function'
+    #
+    # parallel::clusterExport(cl = cl,
+    #                         funs_list,
+    #                         envir = as.environment(asNamespace("mvgam")))
 
     parallel::clusterExport(cl = cl,
-                            funs_list,
+                            unclass(lsf.str(envir = asNamespace("mvgam"),
+                                            all = TRUE)),
                             envir = as.environment(asNamespace("mvgam")))
 
     fc_preds <- parallel::parLapply(cl = cl, seq_len(dim(betas)[1]), function(i){
diff --git a/R/sim_mvgam.R b/R/sim_mvgam.R
index 558c4ebe..5983d381 100644
--- a/R/sim_mvgam.R
+++ b/R/sim_mvgam.R
@@ -360,7 +360,7 @@ sim_mvgam = function(T = 100,
         (obs_trends[,x] * trend_rel)
 
     } else {
-      yseason <- as.vector(scale(stl(ts(rnorm(T, glob_season, sd = 2),
+      yseason <- as.vector(scale(stats::stl(ts(rnorm(T, glob_season, sd = 2),
                                         frequency = freq), 'periodic')$time.series[,1]))
       dynamics <- (yseason * (1 - trend_rel)) +
         (obs_trends[,x] * trend_rel)
diff --git a/src/RcppExports.gcda b/src/RcppExports.gcda
deleted file mode 100644
index 96b4a589..00000000
Binary files a/src/RcppExports.gcda and /dev/null differ
diff --git a/src/trend_funs.gcda b/src/trend_funs.gcda
deleted file mode 100644
index 0c571c46..00000000
Binary files a/src/trend_funs.gcda and /dev/null differ
diff --git a/tests/testthat/test-RW.R b/tests/testthat/test-RW.R
index 98925bf2..bf76e0fa 100644
--- a/tests/testthat/test-RW.R
+++ b/tests/testthat/test-RW.R
@@ -58,20 +58,20 @@ test_that("VARMAs are set up correctly", {
                  run_model = FALSE)
   expect_true(inherits(var, 'mvgam_prefit'))
 
-  var <- mvgam(y ~ s(series, bs = 're') +
+  var <- SW(mvgam(y ~ s(series, bs = 're') +
                  gp(time, c = 5/4, k = 20) - 1,
                trend_model = VAR(),
                data = gaus_data$data_train,
                family = gaussian(),
-               run_model = FALSE)
+               run_model = FALSE))
   expect_true(inherits(var, 'mvgam_prefit'))
 
-  varma <- mvgam(y ~ s(series, bs = 're') +
+  varma <- SW(mvgam(y ~ s(series, bs = 're') +
                    s(season, bs = 'cc') - 1,
                  trend_model = 'VARMA',
                  data = gaus_data$data_train,
                  family = gaussian(),
-                 run_model = FALSE)
+                 run_model = FALSE))
 
   expect_true(any(grepl('// unconstrained ma inverse partial autocorrelations',
                         varma$model_file, fixed = TRUE)))