Jules

Jules is a statistical computing library that aims to provide R-like experience in modern C++.

Currently, it focus on a versatile, yet easy-to-use multi-dimensional array implementation with some common statistical functionalities.

A implementation of data frames and their common operations inspired by Tidyverse is also intended, but it will probably require many features to come to C++ in the next years.

Dependencies

• C++17 compiler
  • Tested on GCC 7.4.0 and Clang 7.0.0
• debug_assert
• range-v3 (soon to be superseded by C++20 Ranges)
• CBLAS (optional)
• Catch (testing)

Installation

If you intend to use BLAS optimized functions, make sure CBLAS is installed.

Recommended: Local Git Submodule

The recommended installation of the library is including it as a submodule of your Git project.

In your project, do

$ git submodule add https://github.com/verri/jules.git external/jules

Local copy

Download the latest release and unzip it.

System-wide installation

For Arch Linux users, install the AUR package jules-git via pacaur, makepkg, or any other method of your preference.

System-wide installation is recommended only if you are sure about what you doing.

Clone the repository.

$ git clone https://github.com/verri/jules.git
$ cd jules

Install via CMake 3.0+.

$ cmake -H. -Bbuild
$ cmake --build build --target install

Usage and Compilation

Jules is a header-only library, so you just need to instruct the compiler its location.

Recommended: CMake 3.0+

If you installed the library as a submodule, include the following line in your CMakeLists.txt

add_subdirectory(external/jules)
target_link_libraries(my-target PRIVATE jules)

If you installed system-wide, use

find_package(Cool REQUIRED)
target_link_libraries(my-target PRIVATE ${Cool_LIBRARY})

If using BLAS, make sure to link to your target.

find_package(BLAS REQUIRED)
target_link_libraries(my-target PRIVATE ${BLAS_LIBRARIES})

Compiler flags

You can directly specify the compiler flags:

CXXFLAGS="-std=c++17 -isystem/path/to/release/include"

If using BLAS, make sure to link it using -lblas or -lcblas, depending on your system.

Documentation

Generated with standardese here (not up-to-date, since standardese is not progressing very well).

Soon, we should provide a cheatsheet with a summary of functionalities. In the meantime, the examples in the test folder should suffice.

Acknowledgements

This project is supported by FAPESP as part of the project "High level data classification based on complex network applied to invariant pattern recognition" (2013/25876-6).