diff --git a/cran-comments.md b/cran-comments.md
index a5436f72..30ccded0 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,3 +1,42 @@
+## Version 0.1.2
+
+I have run R CMD check and have also checked for memory leaks using R CMD check --use-valgrind aorsf_0.1.2.tar.gz.
+
+### Valgrind check results (abbreviated)
+
+==2062== LEAK SUMMARY:
+==2062==    definitely lost: 0 bytes in 0 blocks
+==2062==    indirectly lost: 0 bytes in 0 blocks
+==2062==      possibly lost: 0 bytes in 0 blocks
+==2062==    still reachable: 144,402,034 bytes in 45,757 blocks
+==2062==         suppressed: 0 bytes in 0 blocks
+
+==2062== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
+
+### R CMD check results
+
+   
+### R CMD check results 
+
+Duration: 2m 56.3s
+
+0 errors ✔ | 0 warnings ✔ | 0 notes ✔
+
+R CMD check succeeded
+
+### Downstream dependencies
+
+I have also run R CMD check on downstream dependencies of `aorsf`: 
+
+- collapse
+- data.table
+- lifecycle
+- R6
+- Rcpp
+- utils
+
+All packages passed.
+
 ## Version 0.1.1
 
 Update, October 25: Thank you for helping me with this. I have tidied up threads and avoided usage of the `data.table` functions that were creating possible memory leaks.  I have checked this submission locally with valgrind and on rhub, with both indicating 0 memory leaks. However, if this submission does not pass on your end, I would like to request an extension on the October 28th deadline.
diff --git a/man/orsf.Rd b/man/orsf.Rd
index 6014ea2b..3360c4bd 100644
--- a/man/orsf.Rd
+++ b/man/orsf.Rd
@@ -366,14 +366,6 @@ data that were not used to train it, i.e., testing data.
 library(magrittr) # for \%>\%
 }\if{html}{\out{</div>}}
 
-\if{html}{\out{<div class="sourceCode">}}\preformatted{## 
-## Attaching package: 'magrittr'
-
-## The following objects are masked from 'package:testthat':
-## 
-##     equals, is_less_than, not
-}\if{html}{\out{</div>}}
-
 \code{orsf()} is the entry-point of the \code{aorsf} package. It can be used to
 fit classification, regression, and survival forests.
 
@@ -398,7 +390,7 @@ penguin_fit
 ##    N predictors per node: 3
 ##  Average leaves per tree: 5.4
 ## Min observations in leaf: 5
-##           OOB stat value: 0.99
+##           OOB stat value: 0.98
 ##            OOB stat type: AUC-ROC
 ##      Variable importance: anova
 ## 
@@ -423,9 +415,9 @@ bill_fit
 ##                  N trees: 5
 ##       N predictors total: 7
 ##    N predictors per node: 3
-##  Average leaves per tree: 45.2
+##  Average leaves per tree: 49.4
 ## Min observations in leaf: 5
-##           OOB stat value: 0.74
+##           OOB stat value: 0.72
 ##            OOB stat type: RSQ
 ##      Variable importance: anova
 ## 
@@ -454,10 +446,10 @@ pbc_fit
 ##                  N trees: 5
 ##       N predictors total: 17
 ##    N predictors per node: 5
-##  Average leaves per tree: 21.4
+##  Average leaves per tree: 21
 ## Min observations in leaf: 5
 ##       Min events in leaf: 1
-##           OOB stat value: 0.75
+##           OOB stat value: 0.74
 ##            OOB stat type: Harrell's C-index
 ##      Variable importance: anova
 ## 
@@ -504,7 +496,7 @@ 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 2.142239 secs
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## Time difference of 2.185202 secs
 }\if{html}{\out{</div>}}
 \enumerate{
 \item If fitting multiple forests, use the blueprint along with
@@ -575,12 +567,12 @@ 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    0.0541
-## 2 brier_survival standard         1000    0.0963
-## 3 brier_survival standard         1500    0.0878
-## 4 brier_survival standard         2000    0.0970
-## 5 brier_survival standard         2500    0.143 
-## 6 brier_survival standard         3000    0.198
+## 1 brier_survival standard          500    0.0569
+## 2 brier_survival standard         1000    0.0884
+## 3 brier_survival standard         1500    0.0888
+## 4 brier_survival standard         2000    0.0682
+## 5 brier_survival standard         2500    0.128 
+## 6 brier_survival standard         3000    0.189
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{roc_scores <- test_pred \%>\% 
@@ -592,12 +584,12 @@ 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     0.959
-## 2 roc_auc_survival standard         1000     0.913
-## 3 roc_auc_survival standard         1500     0.955
-## 4 roc_auc_survival standard         2000     0.941
-## 5 roc_auc_survival standard         2500     0.912
-## 6 roc_auc_survival standard         3000     0.907
+## 1 roc_auc_survival standard          500     0.931
+## 2 roc_auc_survival standard         1000     0.927
+## 3 roc_auc_survival standard         1500     0.945
+## 4 roc_auc_survival standard         2000     0.972
+## 5 roc_auc_survival standard         2500     0.910
+## 6 roc_auc_survival standard         3000     0.877
 }\if{html}{\out{</div>}}
 }
 }