Result for 0254BC662CC227D8AA17986E00160AB4BC656725

Query result

Key Value
FileName./usr/lib/R/site-library/Rcpp/examples/ConvolveBenchmarks/Makefile
FileSize968
MD577760B9EF8CFA285959C7148F65D8DA3
SHA-10254BC662CC227D8AA17986E00160AB4BC656725
SHA-256E4CEEDEFDE6B9A86E24F58DBA58AD19C969E2C576FFD88B13FD45923C610F8CD
SSDEEP24:gNE8peRnE8jv/mFpRujT+u34d/rZHt8dD:gbG6Fu2uIdlHt8dD
TLSHT136117A3736981DFF2B381CA4A19E308AE7422202A512566E84754E5F710D2FDF06AE3F
hashlookup:parent-total2
hashlookup:trust60

Network graph view

Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize1968716
MD549272A9B6FABB47746A7FF6F3DC9F119
PackageDescriptionGNU R package for Seamless R and C++ Integration The Rcpp package provides a C++ library which facilitates the integration of R and C++. . R data types (SEXP) are matched to C++ objects in a class hierarchy. All R types are supported (vectors, functions, environment, etc ...) and each type is mapped to a dedicated class. For example, numeric vectors are represented as instances of the Rcpp::NumericVector class, environments are represented as instances of Rcpp::Environment, functions are represented as Rcpp::Function, etc ... The "Rcpp-introduction" vignette provides a good entry point to Rcpp. . Conversion from C++ to R and back is driven by the templates Rcpp::wrap and Rcpp::as which are highly flexible and extensible, as documented in the "Rcpp-extending" vignette. . Rcpp also provides Rcpp modules, a framework that allows exposing C++ functions and classes to the R level. The "Rcpp-modules" vignette details the current set of features of Rcpp-modules. . Rcpp includes a concept called Rcpp sugar that brings many R functions into C++. Sugar takes advantage of lazy evaluation and expression templates to achieve great performance while exposing a syntax that is much nicer to use than the equivalent low-level loop code. The "Rcpp-sugar" vignette gives an overview of the feature. . Rcpp attributes provide a high-level syntax for declaring C++ functions as callable from R and automatically generating the code required to invoke them. Attributes are intended to facilitate both interactive use of C++ within R sessions as well as to support R package development. Attributes are built on top of Rcpp modules and their implementation is based on previous work in the inline package. . Many examples are included, and around 891 unit tests in 430 unit test functions provide additional usage examples. . An earlier version of Rcpp, containing what we now call the 'classic Rcpp API' was written during 2005 and 2006 by Dominick Samperi. This code has been factored out of Rcpp into the package RcppClassic and it is still available for code relying on this interface. New development should use this package instead. . Additional documentation is available via the paper by Eddelbuettel and Francois (2011, JSS) paper and the book by Eddelbuettel (2013, Springer); see 'citation("Rcpp")' for details.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-rcpp
PackageSectiongnu-r
PackageVersion0.11.0-1
SHA-1A3A8E30955E8141E1790BED4F3C15B9DA6B738B3
SHA-256D7B8D766D4741F43C589F3FD5F6033EDAD914DFA588A019F280F7F97DD658E95
Key Value
FileSize1972304
MD5DAEAC0D825707B428A2452712C17A1F0
PackageDescriptionGNU R package for Seamless R and C++ Integration The Rcpp package provides a C++ library which facilitates the integration of R and C++. . R data types (SEXP) are matched to C++ objects in a class hierarchy. All R types are supported (vectors, functions, environment, etc ...) and each type is mapped to a dedicated class. For example, numeric vectors are represented as instances of the Rcpp::NumericVector class, environments are represented as instances of Rcpp::Environment, functions are represented as Rcpp::Function, etc ... The "Rcpp-introduction" vignette provides a good entry point to Rcpp. . Conversion from C++ to R and back is driven by the templates Rcpp::wrap and Rcpp::as which are highly flexible and extensible, as documented in the "Rcpp-extending" vignette. . Rcpp also provides Rcpp modules, a framework that allows exposing C++ functions and classes to the R level. The "Rcpp-modules" vignette details the current set of features of Rcpp-modules. . Rcpp includes a concept called Rcpp sugar that brings many R functions into C++. Sugar takes advantage of lazy evaluation and expression templates to achieve great performance while exposing a syntax that is much nicer to use than the equivalent low-level loop code. The "Rcpp-sugar" vignette gives an overview of the feature. . Rcpp attributes provide a high-level syntax for declaring C++ functions as callable from R and automatically generating the code required to invoke them. Attributes are intended to facilitate both interactive use of C++ within R sessions as well as to support R package development. Attributes are built on top of Rcpp modules and their implementation is based on previous work in the inline package. . Many examples are included, and around 891 unit tests in 430 unit test functions provide additional usage examples. . An earlier version of Rcpp, containing what we now call the 'classic Rcpp API' was written during 2005 and 2006 by Dominick Samperi. This code has been factored out of Rcpp into the package RcppClassic and it is still available for code relying on this interface. New development should use this package instead. . Additional documentation is available via the paper by Eddelbuettel and Francois (2011, JSS) paper and the book by Eddelbuettel (2013, Springer); see 'citation("Rcpp")' for details.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-rcpp
PackageSectiongnu-r
PackageVersion0.11.0-1
SHA-1DAE743A46822CC42B30856F901655B1AD818F3B0
SHA-2568E789FBDE2BE810233C496401582F5D06DBBA0A3D1CED5FC10EE2384432A472B