This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms

Testing the additional predictive value of high-dimensional data
Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>,
Torsten Hothorn <torsten.hothorn@stat.uni-muenchen.de>.
'globalboosttest' implements a permutation-based testing
procedure to globally test the (additional) predictive value of
a large set of predictors given that a small set of predictors
is already available. Currently, 'globalboosttest' supports
binary outcomes (via logistic regression) and survival outcomes
(via Cox regression). It is based on boosting regression as
implemented in the package 'mboost'.

Identify Global Objects in R Expressions
c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"))
Identifies global ("unknown" or "free") objects in R expressions
by code inspection using various strategies, e.g. conservative or liberal.
The objective of this package is to make it as simple as possible to
identify global objects for the purpose of exporting them in distributed
compute environments.

c(
person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"),
email="henrikb@braju.com"),
person("Davis","Vaughan", role="ctb",
email="davis@rstudio.com"))
Identifies global ("unknown" or "free") objects in R expressions
by code inspection using various strategies (ordered, liberal, or
conservative).  The objective of this package is to make it as simple as
possible to identify global objects for the purpose of exporting them in
parallel, distributed compute environments.
Henrik Bengtsson [aut, cre, cph],
Davis Vaughan [ctb]

Identifies global ("unknown") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal.  The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.

Identifies global ("unknown") objects in R expressions by code
inspection using various strategies, e.g. conservative or liberal. The objective
of this package is to make it as simple as possible to identify global objects
for the purpose of exporting them in distributed compute environments.