Simulation and Inference for Stochastic Differential Equations
Brownian motion, Brownian bridge, and geometric Brownian motion simula...
Volatility change-point estimator for diffusion processes
Simulation of diffusion bridge
Approximated conditional law of a diffusion process by Elerian's metho...
Approximated conditional law of a diffusion process
Approximated conditional law of a diffusion process by Kessler's metho...
Approximated conditional law of a diffusion process by Ozaki's method
Approximated conditional law of a diffusion process by the Shoji-Ozaki...
Pedersen's simulated transition density
Euler approximation of the likelihood
Generalized method of moments estimator
Ait-Sahalia Hermite polynomial expansion approximation of the likeliho...
Nonparametric invariant density, drift, and diffusion coefficient esti...
Linear martingale estimating function
Markov Operator distance for clustering diffusion processes.
Black-Scholes-Merton or geometric Brownian motion process conditional ...
Conditional law of the Cox-Ingersoll-Ross process
Ornstein-Uhlenbeck or Vasicek process conditional law
Cox-Ingersoll-Ross process stationary law
Ornstein-Uhlenbeck or Vasicek process stationary law
Simulation of stochastic differential equation
Akaike's information criterion for diffusion processes
Phi-Divergences test for diffusion processes
Pedersen's approximation of the likelihood
Simple estimating functions of types I and II
Simple estimating function based on the infinitesimal generator a the ...
Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY.