Rdocumentation
powered by
Learn R Programming
⚠️
There's a newer version (2.0.18) of this package.
Take me there.
sde (version 2.0.15)
Simulation and Inference for Stochastic Differential Equations
Description
Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY.
Copy Link
Link to current version
Version
Version
2.0.18
2.0.15
2.0.14
2.0.13
2.0.10
2.0.9
2.0.8
2.0.7
2.0.6
2.0.5
2.0.4
2.0.3
2.0.1
2.0.0
1.9.33
1.9.32
1.9.31
1.9.28
1.9.27
1.9.26
1.9.24
1.9.23
1.9.21
1.9.20
1.9.18
1.9.16
1.9.14
1.9.13
1.9.12
1.9.11
1.9.9
1.9.8
1.9.5
1.9.4
1.8
1.4
1.1
1.0
Install
install.packages('sde')
Monthly Downloads
1,989
Version
2.0.15
License
GPL (>= 2)
Maintainer
Stefano Gary King
Last Published
April 13th, 2016
Functions in sde (2.0.15)
Search all functions
dcKessler
Approximated conditional law of a diffusion process by Kessler's method
dcElerian
Approximated conditional law of a diffusion process by Elerian's method
sde.sim
Simulation of stochastic differential equation
sdeAIC
Akaike's information criterion for diffusion processes
rcCIR
Conditional law of the Cox-Ingersoll-Ross process
simple.ef2
Simple estimating function based on the infinitesimal generator a the diffusion process
quotes
Daily closings of 20 financial time series from 2006-01-03 to 2007-12-31
rsCIR
Cox-Ingersoll-Ross process stationary law
DBridge
Simulation of diffusion bridge
linear.mart.ef
Linear martingale estimating function
dcEuler
Approximated conditional law of a diffusion process
simple.ef
Simple estimating functions of types I and II
rcOU
Ornstein-Uhlenbeck or Vasicek process conditional law
DWJ
Weekly closings of the Dow-Jones industrial average
SIMloglik
Pedersen's approximation of the likelihood
dcOzaki
Approximated conditional law of a diffusion process by Ozaki's method
dcShoji
Approximated conditional law of a diffusion process by the Shoji-Ozaki method
rcBS
Black-Scholes-Merton or geometric Brownian motion process conditional law
cpoint
Volatility change-point estimator for diffusion processes
ksmooth
Nonparametric invariant density, drift, and diffusion coefficient estimation
MOdist
Markov Operator distance for clustering diffusion processes.
EULERloglik
Euler approximation of the likelihood
sdeDiv
Phi-Divergences test for diffusion processes
dcSim
Pedersen's simulated transition density
BM
Brownian motion, Brownian bridge, and geometric Brownian motion simulators
gmm
Generalized method of moments estimator
HPloglik
Ait-Sahalia Hermite polynomial expansion approximation of the likelihood
rsOU
Ornstein-Uhlenbeck or Vasicek process stationary law