synthetic.dataset: Generate Synthetic Dataset
In rossklin/SimpleSDESampler: Simple SDE Sampler

Simulate sample trajectories of an SDE and store the results in a time.table

synthetic.dataset(num.entities = 10, tmax = 2, steps = 100 * tmax,
  process.noise.sd = 0, observation.noise.sd = 0, do.standardise = F,
  initial.generator = function(i) {     rnorm(3) },
  det.deriv = examples.gensys.det.lorenz,
  jacobian = examples.gensys.jacob.lorenz, at.times = NULL,
  include.derivatives = FALSE, save.to = NULL, retries = 10)

`num.entities`	Number of sample trajectories to generate.
`tmax`	End of the time interval to integrate the SDE over.
`steps`	Number of time steps to use in the integration.
`process.noise.sd`	Standard deviation of the brownian motion component.
`observation.noise.sd`	Standard deviation of synthetic observation noise.
`do.standardise`	Standardise the output?
`initial.generator`	Function that takes an index and generates a starting point for a sample SDE trajectory.
`det.deriv`	Function f: (m x n matrix of m states, scalar t time) -> m x n matrix of m state derivs computing the deterministic component of the dynamic given the state.
`stoch.deriv`	Function g: (1 x n matrix state, scalar t time) -> n x n matrix of noise weights, computing the stochastic coefficient of the time dynamic the state.
`jacobian`	Function J: (1 x n matrix state, scalar t time) -> n x n matrix d fi / d xj computing the jacobian of det.deriv with respect to the state variables.
`at.times`	Array of times to include in the output. Produces sample trajectories for an SDE on Ito form: dx(t) = f(x(t), t) dt + g(x(t), t) e(t) sqrt(dt) where det.deriv is f and stoch.deriv is g