Running ‘birds_Biometrics.R’ [13s/17s]
  Comparing ‘birds_Biometrics.Rout’ to ‘birds_Biometrics.Rout.save’ ... OK
  Running ‘bugfixes.R’ [71s/88s]
  Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
  Running ‘regtest-baselearner.R’ [35s/44s]
  Comparing ‘regtest-baselearner.Rout’ to ‘regtest-baselearner.Rout.save’ ...
42,43c42
< In bols(xn) :
<   base-learner contains missing values;
---
> In bols(xn) : base-learner contains missing values;
61,62c60
< In bols(xf) :
<   base-learner contains missing values;
---
> In bols(xf) : base-learner contains missing values;
73,74c71
< In bols(xf, intercept = FALSE) :
<   base-learner contains missing values;
---
> In bols(xf, intercept = FALSE) : base-learner contains missing values;
100,101c97
< In bols(xn, xf) :
<   base-learner contains missing values;
---
> In bols(xn, xf) : base-learner contains missing values;
246,247c242
< In df2lambda(X, df = 4, weights = rep(1, 100)) :
<   'df' too large:
---
> In df2lambda(X, df = 4, weights = rep(1, 100)) : 'df' too large:
299,300c294
< In bbs(x, df = ncol(X)) :
<   base-learner contains missing values;
---
> In bbs(x, df = ncol(X)) : base-learner contains missing values;
304,305c298
< In bols(X, df = ncol(X)) :
<   base-learner contains missing values;
---
> In bols(X, df = ncol(X)) : base-learner contains missing values;
  Running ‘regtest-blackboost.R’ [77s/98s]
  Comparing ‘regtest-blackboost.Rout’ to ‘regtest-blackboost.Rout.save’ ... OK
  Running ‘regtest-family.R’ [19s/23s]
  Comparing ‘regtest-family.Rout’ to ‘regtest-family.Rout.save’ ... OK
  Running ‘regtest-gamboost.R’ [9s/11s]
  Comparing ‘regtest-gamboost.Rout’ to ‘regtest-gamboost.Rout.save’ ... OK
  Running ‘regtest-glmboost.R’ [13s/16s]
  Comparing ‘regtest-glmboost.Rout’ to ‘regtest-glmboost.Rout.save’ ... OK
  Running ‘regtest-hatmatrix.R’ [2s/3s]
  Comparing ‘regtest-hatmatrix.Rout’ to ‘regtest-hatmatrix.Rout.save’ ... OK
  Running ‘regtest-varimp.R’ [4s/5s]
  Comparing ‘regtest-varimp.Rout’ to ‘regtest-varimp.Rout.save’ ... OK