Installation guide

If GPU acceleration is required, make sure the CUDA toolkit is installed first.

The C library

The AccRQA library is compiled from source using CMake. Although, AccRQA can be compiled in Linux and Windows, we strongly suggest using Linux.

Linux

Clone AccRQA repository using:

git clone https://github.com/KAdamek/AccRQA.git

From the top-level directory of the AccRQA, run the following commands to compile and install the library:

mkdir build
cd build
cmake [OPTIONS] ../
make
make install

Suitable CUDA architecture is selected by nvcc when compiling because the default option for CUDA is -DCUDA_ARCH="native". However, if you need to specify the CUDA architecture, use -DCUDA_ARCH="x.y" flag, where x and y are major and minor compute capability on NVIDIA GPU. For example, for architecture 7.0, do

cmake -DCUDA_ARCH="7.0" ../

To compile binding to R, you have to specify R library location using -DCMAKE_R_LIB_DIR and location of the R include directory using -DCMAKE_R_INC_DIR. For example

cmake -DCMAKE_R_INC_DIR="/usr/share/R/include" -DCMAKE_R_LIB_DIR="/usr/lib/R/lib/libR.so"  ../

To compile tests use -DBUILD_TESTS=ON which compiles test executable performing a series of tests of supported RQA metrics.

An example application that uses the AccRQA library can be compiled with a flag -DBUILD_APPLICATIONS=ON.

Windows

To compile the AccRQA library from source you will require:

MSVC compiler (part of Visual Studio)
Make for Windows

In command line you can build AccRQA by

mkdir build
cd build
cmake .. [OPTIONS]
cmake --build .
cmake --install .

Python package

Install from source package

We encourage you to install the accrqa package from the source package, as Python binary wheels poorly support GPUs.

pip install accrqa

Install from the binary wheel

Working on it.

Install from the local repository

From the top-level directory, run the following commands to install the Python package:

pip3 install .

The compiled library will be built as part of this step, so it does not need to be installed separately. If extra CMake arguments need to be specified, set the environment variable CMAKE_ARGS first, for example:

CMAKE_ARGS="-DCUDA_ARCH=7.0" pip3 install .

Uninstalling

The Python package can be uninstalled using:

pip3 uninstall accrqa

Installing on Windows

To install accrqa package on Windows the easiest way is to use the binary wheels. To install accrqa Python package on Windows from source you need to have

Windows requirements:

MSVC compiler (part of Visual Studio)
Make for Windows

To install from PiPy using pip:

py -m pip install accrqa

To install from the local repository in command line:

py -m pip install .

R package

System Requirements

To install and use the AccRQA R package, the following system requirements apply:

R version: 4.0 or newer.
Operating system: Linux.
Build tools: gcc, g++, make (usually provided by build-essentials).
(Optional) CUDA Toolkit (e.g., 11.x or newer) — required for GPU acceleration.
- Ensure nvcc and cuda_runtime.h are available in the environment.
- Set CUDA_HOME if the toolkit is not in the default path.

You can install the AccRQA R package either from a downloaded source archive (e.g., .tar.gz or .zip) or directly within RStudio.

Option 1: Install from .zip or .tar.gz in RStudio

Open RStudio.

Go to Tools → Install Packages.

From the box Install from select “Package Archive File (.tar.gz / .zip)” as the sources.

Click Browse, and select the downloaded file (e.g., AccRQA_x.y.z.tar.gz where the x.y.z refers to the specific version number).

Click Install.

Option 2: Install from the Command Line

The downloaded archive can be install by using the R console:

install.packages("AccRQA_x.y.z.tar.gz", repos = NULL, type = "source")

or from your system’s terminal:

R CMD INSTALL AccRQA_x.y.z.tar.gz