diff --git a/man/orsf.Rd b/man/orsf.Rd
index da540775..60d1de57 100644
--- a/man/orsf.Rd
+++ b/man/orsf.Rd
@@ -520,12 +520,12 @@ The AUC values, from highest to lowest:
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{##    model times       AUC         se     lower     upper
-## 1:   net  1788 0.9134593 0.02079935 0.8726933 0.9542253
-## 2:   cph  1788 0.9109155 0.02111657 0.8695278 0.9523032
-## 3: accel  1788 0.9099638 0.02122647 0.8683607 0.9515669
-## 4:   rlt  1788 0.9069752 0.02132529 0.8651783 0.9487720
-## 5: rando  1788 0.9023489 0.02218936 0.8588586 0.9458393
-## 6:   pca  1788 0.8994220 0.02201713 0.8562692 0.9425748
+## 1:   net  1788 0.9179396 0.02012887 0.8784877 0.9573915
+## 2: accel  1788 0.9106396 0.02076004 0.8699507 0.9513286
+## 3:   cph  1788 0.9061167 0.02277540 0.8614777 0.9507556
+## 4:   rlt  1788 0.9012605 0.02178982 0.8585533 0.9439678
+## 5: rando  1788 0.8997729 0.02201363 0.8566270 0.9429188
+## 6:   pca  1788 0.8996927 0.02245483 0.8556821 0.9437034
 }\if{html}{\out{</div>}}
 
 And the indices of prediction accuracy:
@@ -534,12 +534,12 @@ And the indices of prediction accuracy:
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{##         model times       IPA
-## 1:        net  1788 0.4916815
-## 2:        cph  1788 0.4833913
-## 3:      accel  1788 0.4749974
-## 4:        rlt  1788 0.4630984
-## 5:        pca  1788 0.4371223
-## 6:      rando  1788 0.4258456
+## 1:        net  1788 0.5020652
+## 2:        cph  1788 0.4759061
+## 3:      accel  1788 0.4743392
+## 4:        pca  1788 0.4398468
+## 5:        rlt  1788 0.4373910
+## 6:      rando  1788 0.4219209
 ## 7: Null model  1788 0.0000000
 }\if{html}{\out{</div>}}
 
@@ -651,29 +651,29 @@ glimpse(results)
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{## Rows: 276
 ## Columns: 23
-## $ id          <int> 3, 39, 43, 48, 50, 54, 64, 66, 78, 80, 83, 114, 131, 141, ~
-## $ trt         <fct> d_penicill_main, d_penicill_main, d_penicill_main, placebo~
-## $ age         <dbl> 70.07255, 55.39220, 48.87064, 49.13621, 53.50856, 39.19781~
-## $ sex         <fct> m, f, f, m, f, f, f, m, f, m, f, m, f, f, f, f, m, f, f, f~
-## $ ascites     <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
-## $ hepato      <fct> 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1~
-## $ spiders     <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
-## $ edema       <fct> 0.5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, ~
-## $ bili        <dbl> 1.4, 0.7, 1.1, 1.9, 1.1, 1.3, 2.1, 1.4, 6.3, 7.2, 1.3, 3.2~
-## $ chol        <int> 176, 282, 361, 259, 257, 288, 373, 427, 436, 247, 250, 259~
-## $ albumin     <dbl> 3.48, 3.00, 3.64, 3.70, 3.36, 3.40, 3.50, 3.70, 3.02, 3.72~
-## $ copper      <int> 210, 52, 36, 281, 43, 262, 52, 105, 75, 269, 48, 208, 74, ~
-## $ alk.phos    <dbl> 516.0, 9066.8, 5430.2, 10396.8, 1080.0, 5487.2, 1009.0, 19~
-## $ ast         <dbl> 96.10, 72.24, 67.08, 188.34, 106.95, 73.53, 150.35, 182.90~
-## $ trig        <int> 55, 111, 89, 178, 73, 125, 188, 171, 104, 91, 100, 78, 104~
-## $ platelet    <int> 151, 563, 203, 214, 128, 254, 178, 123, 236, 360, 81, 268,~
-## $ protime     <dbl> 12.0, 10.6, 10.6, 11.0, 10.6, 11.0, 11.0, 11.0, 10.6, 11.2~
-## $ stage       <ord> 4, 4, 2, 3, 4, 4, 3, 3, 4, 4, 4, 3, 4, 2, 3, 4, 2, 3, 4, 3~
-## $ time        <int> 1012, 2297, 4556, 4427, 2598, 1434, 1487, 4191, 1690, 890,~
-## $ status      <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0~
-## $ pred_aorsf  <dbl> 0.76027848, 0.25291419, 0.06284001, 0.59437152, 0.15286015~
-## $ pred_rfsrc  <dbl> 0.47891074, 0.16833427, 0.05141013, 0.46526027, 0.06438684~
-## $ pred_ranger <dbl> 0.61304990, 0.13930022, 0.03715869, 0.48395613, 0.04959462~
+## $ id          <int> 16, 29, 43, 62, 79, 82, 103, 105, 111, 114, 115, 139, 141,~
+## $ trt         <fct> placebo, placebo, d_penicill_main, placebo, d_penicill_mai~
+## $ age         <dbl> 40.44353, 63.87680, 48.87064, 60.70637, 46.51608, 67.31006~
+## $ sex         <fct> f, f, f, f, f, f, f, f, f, m, f, f, f, f, f, f, f, f, f, f~
+## $ ascites     <fct> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~
+## $ hepato      <fct> 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1~
+## $ spiders     <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1~
+## $ edema       <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
+## $ bili        <dbl> 0.7, 0.7, 1.1, 1.3, 0.8, 4.5, 2.5, 1.1, 5.5, 3.2, 0.7, 1.1~
+## $ chol        <int> 204, 370, 361, 302, 315, 472, 188, 464, 528, 259, 303, 328~
+## $ albumin     <dbl> 3.66, 3.78, 3.64, 2.75, 4.24, 4.09, 3.67, 4.20, 4.18, 4.30~
+## $ copper      <int> 28, 24, 36, 58, 13, 154, 57, 38, 77, 208, 81, 159, 59, 76,~
+## $ alk.phos    <dbl> 685.0, 5833.0, 5430.2, 1523.0, 1637.0, 1580.0, 1273.0, 164~
+## $ ast         <dbl> 72.85, 73.53, 67.08, 43.40, 170.50, 117.80, 119.35, 151.90~
+## $ trig        <int> 58, 86, 89, 112, 70, 272, 102, 102, 78, 78, 156, 134, 56, ~
+## $ platelet    <int> 198, 390, 203, 329, 426, 412, 110, 348, 467, 268, 307, 142~
+## $ protime     <dbl> 10.8, 10.6, 10.6, 13.2, 10.9, 11.1, 11.1, 10.3, 10.7, 11.7~
+## $ stage       <ord> 3, 2, 2, 4, 3, 3, 4, 3, 3, 3, 3, 4, 2, 2, 3, 4, 2, 3, 4, 4~
+## $ time        <int> 3672, 4509, 4556, 3090, 3707, 3574, 110, 3092, 2350, 3395,~
+## $ status      <dbl> 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0~
+## $ pred_aorsf  <dbl> 0.02210163, 0.12510110, 0.07571520, 0.59580668, 0.12839078~
+## $ pred_rfsrc  <dbl> 0.01861595, 0.15632904, 0.07635485, 0.62281617, 0.19145913~
+## $ pred_ranger <dbl> 0.02143363, 0.13367920, 0.05892584, 0.54481330, 0.21380654~
 }\if{html}{\out{</div>}}
 
 And finish by aggregating the predictions and computing performance in
@@ -699,16 +699,16 @@ counts.
 ## Results by model:
 ## 
 ##     model times  AUC lower upper
-## 1:  aorsf  1826 90.9  86.7  95.1
-## 2:  rfsrc  1826 90.0  85.8  94.3
-## 3: ranger  1826 90.1  86.0  94.3
+## 1:  aorsf  1826 91.0  86.8  95.2
+## 2:  rfsrc  1826 89.2  84.8  93.7
+## 3: ranger  1826 89.6  85.3  94.0
 ## 
 ## Results of model comparisons:
 ## 
-##    times  model reference delta.AUC lower upper   p
-## 1:  1826  rfsrc     aorsf      -0.9  -2.2   0.5 0.2
-## 2:  1826 ranger     aorsf      -0.8  -2.1   0.6 0.3
-## 3:  1826 ranger     rfsrc       0.1  -0.8   1.0 0.8
+##    times  model reference delta.AUC lower upper    p
+## 1:  1826  rfsrc     aorsf      -1.7  -3.4  -0.1 0.04
+## 2:  1826 ranger     aorsf      -1.3  -2.9   0.2 0.08
+## 3:  1826 ranger     rfsrc       0.4  -0.8   1.6 0.52
 
 ## 
 ## NOTE: Values are multiplied by 100 and given in \%.
@@ -722,19 +722,19 @@ counts.
 ## 
 ##         model   times Brier lower upper  IPA
 ## 1: Null model 1826.25  20.5  18.1  22.9  0.0
-## 2:      aorsf 1826.25  10.8   8.5  13.0 47.4
-## 3:      rfsrc 1826.25  11.8   9.6  13.9 42.6
-## 4:     ranger 1826.25  11.7   9.6  13.8 42.7
+## 2:      aorsf 1826.25  10.9   8.7  13.1 46.9
+## 3:      rfsrc 1826.25  12.0   9.9  14.2 41.3
+## 4:     ranger 1826.25  12.0   9.9  14.1 41.5
 ## 
 ## Results of model comparisons:
 ## 
 ##      times  model  reference delta.Brier lower upper            p
-## 1: 1826.25  aorsf Null model        -9.7 -12.4  -7.0 2.820785e-12
-## 2: 1826.25  rfsrc Null model        -8.7 -11.0  -6.4 5.857526e-14
-## 3: 1826.25 ranger Null model        -8.7 -11.1  -6.4 1.380943e-13
-## 4: 1826.25  rfsrc      aorsf         1.0   0.2   1.8 1.507974e-02
-## 5: 1826.25 ranger      aorsf         1.0   0.3   1.7 8.236836e-03
-## 6: 1826.25 ranger      rfsrc        -0.0  -0.5   0.4 9.336601e-01
+## 1: 1826.25  aorsf Null model        -9.6 -12.2  -7.0 9.364941e-13
+## 2: 1826.25  rfsrc Null model        -8.5 -10.7  -6.2 2.074175e-13
+## 3: 1826.25 ranger Null model        -8.5 -10.8  -6.2 3.712823e-13
+## 4: 1826.25  rfsrc      aorsf         1.1   0.3   2.0 1.075856e-02
+## 5: 1826.25 ranger      aorsf         1.1   0.3   1.9 4.825778e-03
+## 6: 1826.25 ranger      rfsrc        -0.1  -0.6   0.5 8.429772e-01
 
 ## 
 ## NOTE: Values are multiplied by 100 and given in \%.
diff --git a/man/orsf_control_custom.Rd b/man/orsf_control_custom.Rd
index fe1e66dd..d2b4b1d7 100644
--- a/man/orsf_control_custom.Rd
+++ b/man/orsf_control_custom.Rd
@@ -70,7 +70,7 @@ fit_rando
 ##  Average leaves per tree: 20
 ## Min observations in leaf: 5
 ##       Min events in leaf: 1
-##           OOB stat value: 0.83
+##           OOB stat value: 0.84
 ##            OOB stat type: Harrell's C-statistic
 ##      Variable importance: anova
 ## 
@@ -110,7 +110,7 @@ prediction accuracy based on out-of-bag predictions:
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{library(riskRegression)
 }\if{html}{\out{</div>}}
 
-\if{html}{\out{<div class="sourceCode">}}\preformatted{## riskRegression version 2023.09.08
+\if{html}{\out{<div class="sourceCode">}}\preformatted{## riskRegression version 2023.03.22
 }\if{html}{\out{</div>}}
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{library(survival)
@@ -135,15 +135,15 @@ The PCA ORSF does quite well! (higher IPA is better)
 ## 
 ##         model times  Brier  lower  upper    IPA
 ## 1: Null model  1788 20.479 18.090 22.868  0.000
-## 2:      rando  1788 11.809  9.727 13.890 42.338
-## 3:        pca  1788 12.967 10.983 14.950 36.683
+## 2:      rando  1788 11.604  9.535 13.673 43.339
+## 3:        pca  1788 12.870 10.872 14.869 37.154
 ## 
 ## Results of model comparisons:
 ## 
 ##    times model  reference delta.Brier   lower  upper            p
-## 1:  1788 rando Null model      -8.670 -10.843 -6.498 5.218847e-15
-## 2:  1788   pca Null model      -7.512  -9.183 -5.842 1.226512e-18
-## 3:  1788   pca      rando       1.158   0.305  2.011 7.810716e-03
+## 1:  1788 rando Null model      -8.875 -11.063 -6.688 1.852437e-15
+## 2:  1788   pca Null model      -7.609  -9.351 -5.866 1.143284e-17
+## 3:  1788   pca      rando       1.267   0.449  2.084 2.381056e-03
 
 ## 
 ## NOTE: Values are multiplied by 100 and given in \%.