  The continuous time random walk model has been widely applied in various
fields, including physics, biology, chemistry, finance, social phenomena, etc.
In this work, we present an algorithm that utilizes a subordinate formula to
generate data of the continuous time random walk in the long time limit. The
algorithm has been validated using commonly employed observables, such as
typical fluctuations of the positional distribution, rare fluctuations, the
mean and the variance of the position, and breakthrough curves with
time-dependent bias, demonstrating a perfect match.
