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Sim.DiffProc (version 2.5)

Simulation of Diffusion Processes

Description

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.

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Version

Install

install.packages('Sim.DiffProc')

Monthly Downloads

678

Version

2.5

License

GPL (>= 2)

Homepage

http://www.r-project.org

Maintainer

Arsalane Guidoum

Last Published

June 9th, 2012

Functions in Sim.DiffProc (2.5)

Adjustment By Normal Distribution

Observation of Geometric Brownian Motion Model

Stochastic system with a cylindric phase plane

Adjustment By Chi-Squared Distribution

Adjustment By Gamma Distribution

Simulation The First Passage Time FPT For A Simulated Diffusion Process

Brownian Motion Property (trajectories brownian between function (+/-)2*sqrt(C*t))

Properties of the stochastic integral and Ito Process [3]

Brownian Motion Property (Invariance by scaling)

Creating The Exponential Martingales Process

Creating Stochastic Process The Cauchy Distribution

Adjustment By F Distribution

Brownian Motion Property

Creating Stochastic Process The Gamma Distribution

Realization a Telegraphic Process

Creating Arithmetic Brownian Motion Model

Adjustment By Exponential Distribution

Empirical Covariance for Brownian Motion

Adjustment By Student t Distribution

Adjustment By Beta Distribution

Creating Stochastic Process The Logistic Distribution

Creating Stochastic Process The Three-Parameter Log Normal Distribution

Brownian Motion Property (Invariance by reversal of time)

Creating Stochastic Process The Weibull Distribution

Creating Random Walk

Observation of Arithmetic Brownian Motion

Adjustment By Log Normal Distribution

Creating White Noise Gaussian

Properties of the stochastic integral and Ito Process [2]

Properties of the stochastic integral and Ito Process [4]

Stratonovitch Integral [1]

Properties of the stochastic integral and Ito Process [5]

Stratonovitch Integral [3]

Properties of the stochastic integral and Ito Process [1]

Adjustment the Density of Random Variable by Kernel Methods

Stratonovitch Integral [2]

Simulation M-Samples of Random Variable X(v[t]) For A Simulated Diffusion Process

Simulation The First Passage Time FPT For Attractive Model(S >= 2,Sigma)

Adjustment By Weibull Distribution

Stochastic Van der Pol oscillator

Observation of Ornstein-Uhlenbeck Process

Creating Ornstein-Uhlenbeck Process

Creating Flow of Geometric Brownian Motion Models

Creating Flow of Hull-White/Vasicek (HWV) Gaussian Diffusion Models

Creating The Hyperbolic Process (by Milstein Scheme)

Creating Flow of Ornstein-Uhlenbeck Process

Parametric Estimation of Ornstein-Uhlenbeck Model (Exact likelihood inference)

Parametric Estimation of Model Black-Scholes (Exact likelihood inference)

Parametric Estimation of Arithmetic Brownian Motion(Exact likelihood inference)

Creating Brownian Motion Model (by a Random Walk)

Creating Flow of Brownian Bridge Model

Creating Flow of Brownian Motion (by a Random Walk)

Creating Brownian Bridge Model

Creating Flow of Brownian Motion (by the Normal Distribution)

Stochastic Lotka-Volterra Model

Creating Flow of The Arithmetic Brownian Motion Model

Creating Hull-White/Vasicek (HWV) Gaussian Diffusion Models

Creating Stochastic Process The Exponential Distribution

Creating Stochastic Process The Beta Distribution

Kolmogorov-Smirnov Tests (Student t Distribution)

Parametric Estimation of Hull-White/Vasicek (HWV) Gaussian Diffusion Models(Exact likelihood inference)

test_ks_dlognorm

Kolmogorov-Smirnov Tests (Log Normal Distribution)

Stochastic harmonic oscillator

test_ks_dweibull

Kolmogorov-Smirnov Tests (Weibull Distribution)

Parametric Estimation of Ornstein-Uhlenbeck Model (Explicit Estimators)

Evolution a Telegraphic Process in Time

Stratonovitch Integral [4]

Creating Bessel process (by Milstein Scheme)

Kolmogorov-Smirnov Tests (Exponential Distribution)

Kernel Density of Random Variable X

Adjustment the Density of Random Variable X by Histograms Methods

Creating Geometric Brownian Motion (GBM) Models

Stochastic Rayleigh oscillator

Stochastic pendulum

Kolmogorov-Smirnov Tests (F Distribution)

Creating Stochastic Process The Log Three-Parameter Gamma Distribution

Simulation The First Passage Time FPT For Attractive Model for Two-Diffusion Processes V(1) and V(2)

Creating Stochastic Process The Student Distribution

Creating Cox-Ingersoll-Ross (CIR) Square Root Diffusion Models (by Milstein Scheme)

Creating Double-Well Potential Model (by Milstein Scheme)

Creating Radial Ornstein-Uhlenbeck Process (by Milstein Scheme)

Stochastic oscillator with additive noise

Creating Pearson Diffusions Process (by Milstein Scheme)

Creating Ahn and Gao model or Inverse of Feller Square Root Models (by Milstein Scheme)

Creating The modified CIR and hyperbolic Process (by Milstein Scheme)

Creating Stochastic Process The Three-Parameter Gamma Distribution

Creating Diffusion Bridge Models (by Euler Scheme)

Creating The Chan-Karolyi-Longstaff-Sanders (CKLS) family of models (by Milstein Scheme)

Predictor-Corrector Method For One-Dimensional SDE

Wright-Fisher Diffusion

Feller Branching Diffusion

Creating Stochastic Process The Log Normal Distribution

Kolmogorov-Smirnov Tests (Gamma Distribution)

Simulation The First Passage Time FPT For Attractive Model(S = 1,Sigma)

Creating Stochastic Process The Gumbel Distribution

Two-Dimensional Attractive Model Model(S = 1,Sigma)

Kolmogorov-Smirnov Tests (Beta Distribution)

Kolmogorov-Smirnov Tests (Chi-Squared Distribution)

Calculating the Empirical Distribution of Random Variable X

Creating Stochastic Process The Log Logistic Distribution

Adjustment the Empirical Distribution of Random Variable X

Creating Stochastic Process The (non-central) Chi-Squared Distribution

Simulation Three-Dimensional Brownian Motion (by the Normal Distribution)

Radial Process Model(S >= 2,Sigma) Or Attractive Model

Simulation Two-Dimensional Brownian Motion (by the Normal Distribution)

Sim.DiffProc-package

Simulation of Diffusion Processes.

Two-Dimensional Attractive Model for Two-Diffusion Processes V(1) and V(2)

Two-Dimensional Attractive Model in Polar Coordinates Model(S >= 2,Sigma)

Simulation Two-Dimensional Brownian Motion (by a Random Walk)

Predictor-Corrector Method For Two-Dimensional SDE

Simulation Three-Dimensional Brownian Motion (by a Random Walk)

Radial Process Model(S = 1,Sigma) Or Attractive Model

Three-Dimensional Attractive Model for Two-Diffusion Processes V(1) and V(2)

Two-Dimensional Attractive Model Model(S >= 2,Sigma)

Two-Dimensional Attractive Model in Polar Coordinates Model(S = 1,Sigma)

Three-Dimensional Attractive Model Model(S >= 2,Sigma)

Creating Stochastic Process The Three-Parameter Weibull Distribution

Numerical Solution of Three-Dimensional SDE

Predictor-Corrector Method For Three-Dimensional SDE

Numerical Solution of Two-Dimensional SDE

Three-Dimensional Attractive Model Model(S = 1,Sigma)

Creating The Jacobi Diffusion Process (by Milstein Scheme)

Creating Stochastic Process The Three-Parameter Log Logistic Distribution

Creating The General Hyperbolic Diffusion (by Milstein Scheme)

Numerical Solution of One-Dimensional SDE

Kolmogorov-Smirnov Tests (Normal Distribution)

Approximated Conditional Law a Diffusion Process

Display a Data Frame in a Tk Text Widget

Histograms of Random Variable X

Creating Constant Elasticity of Variance (CEV) Models (by Milstein Scheme)

Creating Stochastic Process The Generalized Pareto Distribution

Creating Brownian Motion Model (by the Normal Distribution)