diff --git a/R/backends.R b/R/backends.R
index 6870355c..afa082f7 100644
--- a/R/backends.R
+++ b/R/backends.R
@@ -638,10 +638,10 @@ read_csv_as_stanfit <- function(files, variables = NULL,
 
   # Can make LV models slightly more efficient by not filling in zeros in a loop
   if(any(grepl('lv_coefs_raw[i, j] = 0;', stan_file, fixed = TRUE))){
-    starts <- mvgam:::grepws('lv_coefs_raw[i, j] = 0;', stan_file) - 2
-    ends <- mvgam:::grepws('lv_coefs_raw[i, j] = 0;', stan_file) + 2
+    starts <- grepws('lv_coefs_raw[i, j] = 0;', stan_file) - 2
+    ends <- grepws('lv_coefs_raw[i, j] = 0;', stan_file) + 2
     stan_file <- stan_file[-c(starts:ends)]
-    stan_file[mvgam:::grepws('matrix[n_series, n_lv] lv_coefs_raw;', stan_file)] <-
+    stan_file[grepws('matrix[n_series, n_lv] lv_coefs_raw;', stan_file)] <-
       'matrix[n_series, n_lv] lv_coefs_raw = rep_matrix(0, n_series, n_lv);'
   }
 
diff --git a/R/globals.R b/R/globals.R
index 7ca4e689..f96b230e 100644
--- a/R/globals.R
+++ b/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.", "file_name"))
+                         "matches", "time.", "file_name", ".data"))
diff --git a/R/monotonic.R b/R/monotonic.R
index 2e29031b..4d2fd93b 100644
--- a/R/monotonic.R
+++ b/R/monotonic.R
@@ -503,7 +503,7 @@ eval_smoothDotmodDotsmooth = function(smooth,
 
   # set some values to NA if too far from the data
   if (gratia::smooth_dim(smooth) == 2L && (!is.null(dist) && dist > 0)) {
-    eval_sm <- too_far_to_na(smooth,
+    eval_sm <- gratia::too_far_to_na(smooth,
                              input = eval_sm,
                              reference = model[["model"]],
                              cols = c(".estimate", ".se"),
@@ -561,7 +561,7 @@ eval_smoothDotmoiDotsmooth = function(smooth,
 
   # set some values to NA if too far from the data
   if (gratia::smooth_dim(smooth) == 2L && (!is.null(dist) && dist > 0)) {
-    eval_sm <- too_far_to_na(smooth,
+    eval_sm <- gratia::too_far_to_na(smooth,
                              input = eval_sm,
                              reference = model[["model"]],
                              cols = c(".estimate", ".se"),
@@ -626,7 +626,7 @@ eval_smoothDotmoiDotsmooth = function(smooth,
     ## if this is a by variable, filter the by variable for the required
     ## level now
     if (gratia::is_factor_by_smooth(smooth)) {
-      data <- data %>% filter(.data[[by_var]] == gratia::by_level(smooth))
+      data <- data %>% dplyr::filter(.data[[by_var]] == gratia::by_level(smooth))
     }
   }
   data
diff --git a/R/stan_utils.R b/R/stan_utils.R
index 23e7525c..9ceb478b 100644
--- a/R/stan_utils.R
+++ b/R/stan_utils.R
@@ -36,14 +36,14 @@ print.mvgammodel = function(x, ...){
 #' @export
 #' @importFrom brms stancode
 #' @rdname code
-stancode.mvgam_prefit = function(object){
+stancode.mvgam_prefit = function(object, ...){
 
   code(object)
 }
 
 #' @export
 #' @rdname code
-stancode.mvgam = function(object){
+stancode.mvgam = function(object, ...){
 
   code(object)
 }
diff --git a/man/code.Rd b/man/code.Rd
index 468be096..f0e21e20 100644
--- a/man/code.Rd
+++ b/man/code.Rd
@@ -8,9 +8,9 @@
 \usage{
 code(object)
 
-\method{stancode}{mvgam_prefit}(object)
+\method{stancode}{mvgam_prefit}(object, ...)
 
-\method{stancode}{mvgam}(object)
+\method{stancode}{mvgam}(object, ...)
 }
 \arguments{
 \item{object}{An object of class \code{mvgam} or \code{mvgam_prefit},
diff --git a/man/mvgam_marginaleffects.Rd b/man/mvgam_marginaleffects.Rd
index 5564cfdb..16e6648e 100644
--- a/man/mvgam_marginaleffects.Rd
+++ b/man/mvgam_marginaleffects.Rd
@@ -86,6 +86,8 @@ arguments.}
 \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes.
 \item See the Examples section and the \code{\link[marginaleffects:datagrid]{datagrid()}} documentation.
 }
+\item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)}
+\item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)}
 \item string:
 \itemize{
 \item "mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode.
diff --git a/src/RcppExports.o b/src/RcppExports.o
index e8dd19f0..03b01119 100644
Binary files a/src/RcppExports.o and b/src/RcppExports.o differ
diff --git a/src/mvgam.dll b/src/mvgam.dll
index 24fb715d..4ae626bc 100644
Binary files a/src/mvgam.dll and b/src/mvgam.dll differ
diff --git a/src/trend_funs.o b/src/trend_funs.o
index 917270cf..1c0d5a8f 100644
Binary files a/src/trend_funs.o and b/src/trend_funs.o differ