diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 3cd5703a..fb9d4485 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.1.2
-Date: 2024-01-14 22:34:29 UTC
-SHA: e2720672022846736ccc88d783d0f1a3f382df9a
+Date: 2024-01-15 02:07:42 UTC
+SHA: 2f3e67b75fc2de3aff1823040068c854c37a18a9
diff --git a/man/orsf.Rd b/man/orsf.Rd
index 15a56a85..1bd56e89 100644
--- a/man/orsf.Rd
+++ b/man/orsf.Rd
@@ -388,7 +388,7 @@ penguin_fit
 ##                  N trees: 5
 ##       N predictors total: 7
 ##    N predictors per node: 3
-##  Average leaves per tree: 5.6
+##  Average leaves per tree: 4.4
 ## Min observations in leaf: 5
 ##           OOB stat value: 0.99
 ##            OOB stat type: AUC-ROC
@@ -415,9 +415,9 @@ bill_fit
 ##                  N trees: 5
 ##       N predictors total: 7
 ##    N predictors per node: 3
-##  Average leaves per tree: 50.4
+##  Average leaves per tree: 51.2
 ## Min observations in leaf: 5
-##           OOB stat value: 0.72
+##           OOB stat value: 0.77
 ##            OOB stat type: RSQ
 ##      Variable importance: anova
 ## 
@@ -447,10 +447,10 @@ pbc_fit
 ##                  N trees: 5
 ##       N predictors total: 17
 ##    N predictors per node: 5
-##  Average leaves per tree: 21.4
+##  Average leaves per tree: 20
 ## Min observations in leaf: 5
 ##       Min events in leaf: 1
-##           OOB stat value: 0.75
+##           OOB stat value: 0.76
 ##            OOB stat type: Harrell's C-index
 ##      Variable importance: anova
 ## 
@@ -497,7 +497,7 @@ take to fit the forest before you commit to it:
  orsf_time_to_train()
 }\if{html}{\out{</div>}}
 
-\if{html}{\out{<div class="sourceCode">}}\preformatted{## Time difference of 2.426429 secs
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## Time difference of 2.4331 secs
 }\if{html}{\out{</div>}}
 \enumerate{
 \item If fitting multiple forests, use the blueprint along with
@@ -568,12 +568,12 @@ brier_scores
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 6 x 4
 ##   .metric        .estimator .eval_time .estimate
 ##   <chr>          <chr>           <dbl>     <dbl>
-## 1 brier_survival standard          500    0.0275
-## 2 brier_survival standard         1000    0.0658
-## 3 brier_survival standard         1500    0.0480
-## 4 brier_survival standard         2000    0.0623
-## 5 brier_survival standard         2500    0.138 
-## 6 brier_survival standard         3000    0.146
+## 1 brier_survival standard          500    0.0466
+## 2 brier_survival standard         1000    0.0754
+## 3 brier_survival standard         1500    0.0612
+## 4 brier_survival standard         2000    0.0885
+## 5 brier_survival standard         2500    0.133 
+## 6 brier_survival standard         3000    0.141
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{roc_scores <- test_pred \%>\% 
@@ -585,12 +585,12 @@ roc_scores
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## # A tibble: 6 x 4
 ##   .metric          .estimator .eval_time .estimate
 ##   <chr>            <chr>           <dbl>     <dbl>
-## 1 roc_auc_survival standard          500     0.988
-## 2 roc_auc_survival standard         1000     0.959
-## 3 roc_auc_survival standard         1500     0.992
-## 4 roc_auc_survival standard         2000     0.987
-## 5 roc_auc_survival standard         2500     0.908
-## 6 roc_auc_survival standard         3000     0.909
+## 1 roc_auc_survival standard          500     0.947
+## 2 roc_auc_survival standard         1000     0.939
+## 3 roc_auc_survival standard         1500     0.982
+## 4 roc_auc_survival standard         2000     0.961
+## 5 roc_auc_survival standard         2500     0.929
+## 6 roc_auc_survival standard         3000     0.942
 }\if{html}{\out{</div>}}
 }
 }
diff --git a/vignettes/pd.Rmd b/vignettes/pd.Rmd
index f03f34dd..f8f153e8 100644
--- a/vignettes/pd.Rmd
+++ b/vignettes/pd.Rmd
@@ -317,11 +317,11 @@ pbc_orsf$edema_05 <- NULL
 
 ## Find interactions using PD
 
-Random forests are good at using interactions, but less good at telling you about them. Use `orsf_vint()` to apply the method for variable interaction scoring with PD described by Greenwell et al (2018). This can take a little while if you have lots of predictors, so setting `verbose_progress = TRUE` may be helpful.
+Random forests are good at using interactions, but less good at telling you about them. Use `orsf_vint()` to apply the method for variable interaction scoring with PD described by Greenwell et al (2018). This can take a little while if you have lots of predictors.
 
 ```{r}
 
-vint_scores <- orsf_vint(fit_surv, verbose_progress = TRUE)
+vint_scores <- orsf_vint(fit_surv)
 
 vint_scores[1:5]