diff --git a/R/performance.R b/R/performance.R
index 0bad30a5..afc9e07d 100644
--- a/R/performance.R
+++ b/R/performance.R
@@ -214,8 +214,8 @@ get_performance_tbl <- function(trained_model,
 #' @export
 calc_model_sensspec <- function(trained_model, test_data, outcome_colname = NULL) {
   # adapted from https://github.com/SchlossLab/2021-08-09_ROCcurves/blob/8e62ff8b6fe1b691450c953a9d93b2c11ce3369a/ROCcurves.Rmd#L95-L109
-  outcome_colname = check_outcome_column(test_data, outcome_colname)
-  pos_outcome = trained_model$levels[1]
+  outcome_colname <- check_outcome_column(test_data, outcome_colname)
+  pos_outcome <- trained_model$levels[1]
   actual <- is_pos <- tp <- fp <- fpr <- NULL
   probs <- stats::predict(trained_model,
     newdata = test_data,
@@ -389,21 +389,22 @@ NULL
 #'
 #'
 #' calc_baseline_precision(otu_mini_bin,
-#'                         outcome_colname = "dx",
-#'                         pos_outcome = "cancer")
+#'   outcome_colname = "dx",
+#'   pos_outcome = "cancer"
+#' )
 #'
 #'
 #' # if you're not sure which outcome was used as the 'positive' outcome during
 #' # model training, you can access it from the trained model and pass it along:
 #' calc_baseline_precision(otu_mini_bin,
-#'                         outcome_colname = "dx",
-#'                         pos_outcome = otu_mini_bin_results_glmnet$trained_model$levels[1])
-#'
+#'   outcome_colname = "dx",
+#'   pos_outcome = otu_mini_bin_results_glmnet$trained_model$levels[1]
+#' )
 #'
 calc_baseline_precision <- function(dataset,
                                     outcome_colname = NULL,
                                     pos_outcome = NULL) {
-  outcome_colname = check_outcome_column(dataset, outcome_colname)
+  outcome_colname <- check_outcome_column(dataset, outcome_colname)
   npos <- dataset %>%
     dplyr::filter(!!rlang::sym(outcome_colname) == pos_outcome) %>%
     nrow()
diff --git a/tests/testthat/fixtures/train-multi.R b/tests/testthat/fixtures/train-multi.R
index 87913189..9abdbe7e 100644
--- a/tests/testthat/fixtures/train-multi.R
+++ b/tests/testthat/fixtures/train-multi.R
@@ -6,13 +6,13 @@ future::plan(future::multicore, workers = 8)
 otu_data_preproc <- mikropml::otu_data_preproc$dat_transformed
 
 results_list <- future.apply::future_lapply(seq(100, 102), function(seed) {
-    run_ml(otu_data_preproc, "glmnet", seed = seed)
+  run_ml(otu_data_preproc, "glmnet", seed = seed)
 }, future.seed = TRUE)
 saveRDS(results_list, testthat::test_path("fixtures", "results_list.Rds"))
 
 param_grid <- expand.grid(
-    seeds = seq(100, 110),
-    methods = c("glmnet", "rf")
+  seeds = seq(100, 110),
+  methods = c("glmnet", "rf")
 )
 results_mtx <- future.apply::future_mapply(
   function(seed, method) {
diff --git a/vignettes/parallel.Rmd b/vignettes/parallel.Rmd
index 2f5a89a0..66b69576 100644
--- a/vignettes/parallel.Rmd
+++ b/vignettes/parallel.Rmd
@@ -82,9 +82,9 @@ results_multi <- future.apply::future_lapply(seq(100, 102), function(seed) {
 ```
 
 ```{r multi_seeds_load, echo = FALSE}
-results_multi <- readRDS(system.file("tests", "testthat", "fixtures", 
-                                     "results_list.Rds",
-                                     package = "mikropml"
+results_multi <- readRDS(system.file("tests", "testthat", "fixtures",
+  "results_list.Rds",
+  package = "mikropml"
 ))
 ```
 
@@ -140,9 +140,9 @@ results_mtx <- future.apply::future_mapply(
 )
 ```
 ```{r results_mtx, echo = FALSE}
-results_mtx <-  readRDS(system.file("tests", "testthat", "fixtures", 
-                                     "results_mtx.Rds",
-                                     package = "mikropml"
+results_mtx <- readRDS(system.file("tests", "testthat", "fixtures",
+  "results_mtx.Rds",
+  package = "mikropml"
 ))
 ```
 
@@ -224,8 +224,10 @@ sensspec_1 %>%
   theme_bw() +
   theme(legend.title = element_blank())
 
-baseline_precision_otu <- calc_baseline_precision(otu_data_preproc, 
-                                                  "dx", "cancer")
+baseline_precision_otu <- calc_baseline_precision(
+  otu_data_preproc,
+  "dx", "cancer"
+)
 sensspec_1 %>%
   rename(recall = sensitivity) %>%
   ggplot(aes(x = recall, y = precision, )) +