'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.

Bootstrap Functions (Originally by Angelo Canty for S)

Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.

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.

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.

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.

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.

'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.

Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".