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'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.
Random Sampling Distribution C++ Routines for Armadillo
Provides popular sampling distributions C++ routines based in armadillo through a header file approach.
Mesh Generation and Surface Tessellation
Makes the 'Qhull' library < http://www.qhull.org> available in R, in a similar manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this.
'stb' Image C/C++ Header Files
Provides image-related C/C++ header files from the 'stb' single-file libraries for image loading, writing, and resizing.
Expanded Replacement and Extension of the 'optim' Function
Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here. This is the version for CRAN.
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.
Utilities from and Interface to the 'Bioinfo-C' ('BIOS') Library
Provides interface to the 'Bioinfo-C' (internal name: 'BIOS') library and utilities. 'ribiosUtils' is a Swiss-knife for computational biology in drug discovery, providing functions and utilities with minimal external dependency and maximal efficiency.
"Cereal Headers for R and C++ Serialization"
To facilitate using 'cereal' with R via 'cpp11' or 'Rcpp'. 'cereal' is a header-only C++11 serialization library. 'cereal' takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, 'XML', or 'JSON'. 'cereal' was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see < https://uscilab.github.io/cereal/> for more information.
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)