BOUKHETALA Kamal <kboukhetala@usthb.dz>, GUIDOUM Arsalane
<starsalane@gmail.com>
Simulation of diffusion processes and numerical solution
of stochastic differential equations. Analysis of discrete-time
approximations for stochastic differential equations (SDE)
driven by Wiener processes,in financial and actuarial modeling
and other areas of application for example modelling and
simulation of dispersion in shallow water using the attractive
center (K.BOUKHETALA, 1996). Approximated the evolution of
conditional law a diffusion process with three methods Euler,
Kessler and Shoji-Ozaki.  Simulation and statistical analysis
of the first passage time (FPT) and M-samples of the random
variable X(v) given by a simulated diffusion process.

c(person("Arsalane", "Guidoum", role = c("aut", "cre"),
email = "starsalane@gmail.com"),
person("Kamal","Boukhetala",role="aut", email
="kboukhetala@usthb.dz"))
The package Sim.DiffProc is an object created in the R
language for simulation and modeling of stochastic differential
equations (SDEs), and statistical analysis of diffusion
processes solution of SDEs. This package contains many objects
(code/function), for example a numerical methods to find the
solutions to SDEs (one, two and three dimensional), which
simulates a flows trajectories, with good accuracy. Many
theoretical problems on the SDEs have become the object of
practical research, as statistical analysis and simulation of
solution of SDEs, enabled many searchers in different domains
to use these equations to modeling and to analyse practical
problems, in financial and actuarial modeling and other areas
of application, for example modelling and simulate of
dispersion in shallow water using the attractive center
(Boukhetala K, 1996),and the stochastic calculus are applied to
the random oscillators problem in physics.  We hope that the
package presented here and the updated survey on the subject
might be of help for practitioners, postgraduate and PhD
students, and researchers in the field who might want to
implement new methods and ideas using R as a statistical
environment.