Description: It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.

Title: Graphical User Interface for Simulation of Diffusion Processes
Description: A Graphical User Interface for some functions in the

Authors@R: c(person("Guidoum", "Arsalane", role = c("aut", "cre"),
Author: Boukhetala Kamal, Guidoum Arsalane

Title: Simulation of Piecewise Linear Fuzzy Numbers
Description: The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are:  (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package.

Title: Simulation-Based Inference Methods for Infectious Disease Models
Authors@R: c(person("Trevelyan J.", "McKinley", email = "t.mckinley@exeter.ac.uk",
person("Stefan", "Widgren", role = "aut", email = "stefan.widgren@gmail.com",
person("Pavol", "Bauer", role = "cph", comment = 'R/mparse.R'),
person("Robin", "Eriksson", role = "cph", comment = 'R/mparse.R'),
person("Stefan", "Engblom", role = "cph", comment = 'R/mparse.R'))
Description: Provides some code to run simulations of state-space models, and then
use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC)
algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle
filter based particle Markov chain Monte Carlo (PMCMC) algorithm
Also provides functions to plot and summarise the outputs.
Imports: stats, dplyr, purrr, tibble, ggplot2, tidyr, mvtnorm,
grDevices, RColorBrewer, Rcpp, RcppXPtrUtils, coda
Author: Trevelyan J. McKinley [aut, cre],
Stefan Widgren [aut] (Author of 'R/mparse.R'),
Maintainer: Trevelyan J. McKinley <t.mckinley@exeter.ac.uk>