  We introduce a software generator for a class of colored (self-correlated)
and non-Gaussian noise, whose statistics and spectrum depend on two parameters,
$q$ and $\tau$. Inspired by Tsallis' nonextensive formulation of statistical
physics, the so-called $q$-distribution is a handy source of self-correlated
noise for a large range of applications. The $q$-noise -- which tends smoothly
for $q=1$ to Ornstein--Uhlenbeck noise with autocorrelation $\tau$ -- is
generated via a stochastic differential equation, using the Heun method (a
second order Runge--Kutta type integration scheme). The algorithm is
implemented as a stand-alone library in C++, and is made available as open
source in the Github repository. Noise' statistics can be specified handily; by
only varying parameter $q$: it has compact support for $q<1$ (sub-Gaussian
regime) and finite variance up to $q=5/3$ (supra-Gaussian regime). Once $q$ is
fixed, noise' autocorrelation can be tuned independently by means of parameter
$\tau$. The presented qNoise generator provides a readily tool to modeling wide
range of real-world noise types, and is suitable to study the effects of
correlation and deviations from the normal distribution in systems of
stochastic differential equations, key to understand system dynamics in
numerous applications. The effect of noises' statistics on the response of a
range of nonlinear systems is briefly discussed. In many of these examples, the
systems' response turns optimal for some $q\neq1$. Hence, this paper aims to
introduce qNoise generator for C++ at the class level and evaluate the kind of
noise it generates, alongside their use in a range of applications.
