synthetic.dataset.quick: Generate Synthetic Dataset: optimised for Laurent polynomial...
In rossklin/SimpleSDESampler: Simple SDE Sampler

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

synthetic.dataset.quick(num.entities = 10, sys = lpoly_examples_lorenz(),
  tmax = 2, steps = 100 * tmax, process.noise.sd = 0,
  observation.noise.sd = 0, do.standardise = F,
  initial.generator = function(i) {    
  rnorm(lpoly_system_specs(sys)$dimension) }, at.times = NULL,
  include.derivatives = FALSE, save.to = NULL, retries = 10,
  .progress = "text", ...)

`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 (set to negative of value to precache noise buf in R, this can be useful if you want deterministic behaviour with respect to a random seed in R).
`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.
`sys`	lpoly system constructed with lpoly_make_system, see examples.lpsys.lorenz()
`at.times`	Sequence of times to include in the output. Produces sample trajectories for an SDE on Ito form: dx(t) = f(x(t)) dt + g(x(t), t) e(t) sqrt(dt) where det.deriv is f and stoch.deriv is g