In this post, i show how to analyze the forecast error for a structural equation model (sem) by means of a stochastic simulation, and how to perform optimal control. Explore simulation techniques in r for statistical computing, covering stochastic modeling, monte carlo sampling, and performance optimization. Open source academic project aimed at developing a complete environment for estimation and simulation of stochastic processes
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In chapter 21 we examined models for stochastic biological systems
These types of models are an introduction to the study of stochastic differential equations (sdes)
A common theme to sdes is learning how to analyze and visualize randomness, broadly defined. About this book the yuima package is the first comprehensive r framework based on s4 classes and methods which allows for the simulation of stochastic differential equations driven by wiener process, lévy processes or fractional brownian motion, as well as carma, cogarch, and point processes. Introduction to probability with r presents r programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. The r package ssar is a fast implementation of gillespie's stochastic simulation algorithm
It combines r's graphical and statistical capabilities with the speed of c++.
