R-BH
Port variant standard
Summary Boost C++ Header Files
Package version 1.78.0.0
Homepage https://github.com/eddelbuettel/bh
Keywords cran
Maintainer CRAN Automaton
License Not yet specified
Other variants There are no other variants.
Ravenports Buildsheet | History
Ravensource Port Directory | History
Last modified 20 DEC 2021, 02:07:19 UTC
Port created 15 APR 2020, 07:13:19 UTC
Subpackage Descriptions
single BH: Boost C++ Header Files Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN packages. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.78.0-0, the following Boost libraries are included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast' 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config' 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision' 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process' 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.
Configuration Switches (platform-specific settings discarded)
This port has no build options.
Package Dependencies by Type
Build (only) gmake:single:ravensys
R:primary:standard
Runtime (only) R:complete:standard
Download groups
main mirror://CRAN/src/contrib
Distribution File Information
3b9e9d07682013e0c06a396dda176b405eab99a7273eca6c40d1b4c4110e8cb3 13269768 CRAN/BH_1.78.0-0.tar.gz
Ports that require R-BH:standard
R-anytime:standard Anything to 'POSIXct' or 'Date' Converter
R-ddalpha:standard Depth-based classification and calculations