Package index • SDEtools

All functions
`CrossVariation()`	Cross-variation of two stochastic processes
`HMMfilterSDE()`	Estimate states in a scalar stochastic differential equation based on discretization to a HMM
`PolicyIterationRegular()`	Solve the optimal control problem using policy iteration, i.e. find the optimal strategy and value function for the case where the uncontrolled system is given by a subgenerator G0 (either due to discounting or due to absorbing boundaries)
`PolicyIterationSingular()`	Solve the optimal control problem using policy iteration, i.e. find the optimal strategy and value function for the case where the uncontrolled system is given by a generator G0
`QuadraticVariation()`	Discretized quadratic variation of a stochastic process
`QuasiStationaryDistribution()`	Compute the quasi-stationary distribution for a terminating Continuous Time Markov Chain
`StationaryDistribution()`	Compute the stationary distribution for a Continuous Time Markov Chain
`TransientModes()`	Compute the first transient modes (forward and backward) for a terminating Continuous Time Markov Chain
`cell.centers()`	Get center of grid cells for a retangular grid
`dCIR()` `pCIR()` `qCIR()` `rCIR()`	The Cox-Ingersoll-Ross process
`dLinSDE()`	Transition probabilities in a linear SDE dX = AXdt + udt + GdB
`dOU()` `pOU()` `qOU()` `rOU()`	The Ornstein-Uhlenbeck process
`euler()`	Euler simulation of an Ito stochastic differential equation dX = f dt + g dB
`fvade()`	Discretize scalar advection-diffusion equation using finite volumes
`fvade2d()`	Compute generator for a 2D advection-diffusion equation
`heun()`	Heun's method for simulation of a Stratonovich stochastic differential equation dX = f dt + g dB
`itointegral()`	Ito integral of a stochastic process w.r.t. another
`lqr()`	Solve the time-varying LQR (Linear Quadratic Regulator) problem
`lyap()`	Algebraic Lyapunov equation AX+Xt(A)+Q=0
`pack.field()`	Convert a tabulated function on the plane to a vector
`prob2pdf()`	Convert cell probabilities to (average) probability densities
`rBM()`	Simulate a sample path of Brownian motion
`rBrownianBridge()`	Simulate a Brownian bridge
`rvBM()`	Simulate a multivariate Brownian motion
`stochint()`	Integrate one sample path of a stochastic process w.r.t. another, returning the Ito integral, the Stratonovich integral, or the "right hand rule".
`unpack.field()`	Convert a vector to a function on the plane

Reference

All functions