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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.
Linear Predictive Models Based on the LIBLINEAR C/C++ Library
A wrapper around the LIBLINEAR C/C++ library for machine learning (available at < https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.
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)
C-Statistics for Risk Prediction Models with Censored Survival Data
Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011)
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.
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.
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/>.
'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.
C-Like 'getopt' Behavior
Package designed to be used with Rscript to write '#!' shebang scripts that accept short and long flags/options. Many users will prefer using instead the packages optparse or argparse which add extra features like automatically generated help option and usage, support for default values, positional argument support, etc.
Open Source Geometry Engine ('GEOS') C API
Provides the Open Source Geometry Engine ('GEOS') as a C API that can be used to write high-performance C and C++ geometry operations using R as an interface. Headers are provided to make linking to and using these functions from C++ code as easy and as safe as possible. This package contains an internal copy of the 'GEOS' library to guarantee the best possible consistency on multiple platforms.