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Low-Level R to Java Interface
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
R and C/C++ Wrappers to Run the Leiden find_partition() Function
An R to C/C++ interface that runs the Leiden community
detection algorithm to find a basic partition (). It runs the
equivalent of the 'leidenalg' find_partition() function, which is
given in the 'leidenalg' distribution file
'leiden/src/functions.py'. This package includes the
required source code files from the official 'leidenalg'
distribution and functions from the R 'igraph'
package. The 'leidenalg' distribution is available from
< https://github.com/vtraag/leidenalg/>
and the R 'igraph' package is available from
< https://igraph.org/r/>.
The Leiden algorithm is described in the article by
Traag et al. (2019)
Tidy C++ Header-Only Definitions for Parts of the C API of R
Core parts of the C API of R are wrapped in a C++ namespace via a set of inline functions giving a tidier representation of the underlying data structures and functionality using a header-only implementation without additional dependencies.
The 'HighFive' 'C++' Interface to 'HDF5'
A modern idiomatic header-only 'C++'' interface for 'libhdf5'. Original software can be found at < https://github.com/highfive-devs/highfive/>.
'Asio' C++ Header Files
'Asio' is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and released under the 'Boost Software License', Version 1.0.
'ViennaCL' C++ Header Files
'ViennaCL' is a free open-source linear algebra library for computations on many-core architectures (GPUs, MIC) and multi-core CPUs. The library is written in C++ and supports 'CUDA', 'OpenCL', and 'OpenMP' (including switches at runtime). I have placed these libraries in this package as a more efficient distribution system for CRAN. The idea is that you can write a package that depends on the 'ViennaCL' library and yet you do not need to distribute a copy of this code with your package.
Adaptive Multivariate Integration over Hypercubes
R wrappers around the cubature C library of Steven G. Johnson for adaptive multivariate integration over hypercubes and the Cuba C library of Thomas Hahn for deterministic and Monte Carlo integration. Scalar and vector interfaces for cubature and Cuba routines are provided; the vector interfaces are highly recommended as demonstrated in the package vignette.
C++ Classes to Embed R in C++ (and C) Applications
C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well.
Translates an R Function to a C++ Function
Enable translation of a tiny subset of R to C++. The user has to define a R function which gets translated. For a full list of possible functions check the documentation. After translation an R function is returned which is a shallow wrapper around the C++ code. Alternatively an external pointer to the C++ function is returned to the user. The intention of the package is to generate fast functions which can be used as ode-system or during optimization.
Global Optimization by Differential Evolution in C++
An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'.