Introduction

The Bayesian Quantile Regression (BQReg) library is a lightweight C++ implementation of Bayesian quantile regression using the asymmetric Laplace density representation of the problem, with bindings for Python and R.

Features:

  • A header-only C++11 library with OpenMP-accelerated MCMC sampling for parallel computation.

  • Built on the Eigen (version >= 3.4.0) templated linear algebra library for fast and efficient matrix-based computation.

  • Straightforward linking with parallelized BLAS libraries, such as OpenBLAS.

  • Seamless integration with Python and R through Pybind11 and Rcpp modules.

  • Available as a single precision (float) or double precision (double) library.

  • Released under a permissive, non-GPL license.

Author: Keith O’Hara

License: Apache Version 2.0

Installation

The C++ implementation of BQReg is a header-only library. First, clone the library and related submodules:

# clone optim into the current directory
git clone https://github.com/kthohr/BayesianQuantileRegression ./BayesianQuantileRegression

# change directory
cd ./BayesianQuantileRegression

# clone necessary submodules
git submodule update --init

Then simply add the BQReg header files (found under cpp/include) to your project using:

#include "bqreg.hpp"

Beyond the files contained in the Git submodules, the only dependencies are a copy of Eigen (version >= 3.4.0) and a C++14-compatible compiler.

For detailed instructions on how to install the R and Python bindings, see the installation page.

Contents

Guide

API