  The aim of this paper is to discuss an estimation and a simulation method in
the \textsf{R} package YUIMA for a linear regression model driven by a
Student-$t$ L\'evy process with constant scale and arbitrary degrees of
freedom. This process finds applications in several fields, for example
finance, physic, biology, etc. The model presents two main issues. The first is
related to the simulation of a sample path at high-frequency level. Indeed,
only the $t$-L\'evy increments defined on an unitary time interval are
Student-$t$ distributed. In YUIMA, we solve this problem by means of the
inverse Fourier transform for simulating the increments of a Student-$t$
L\'{e}vy defined on a interval with any length. A second problem is due to the
fact that joint estimation of trend, scale, and degrees of freedom does not
seem to have been investigated as yet. In YUIMA, we develop a two-step
estimation procedure that efficiently deals with this issue. Numerical examples
are given in order to explain methods and classes used in the YUIMA package.
