estSDE_single: Bayesian stimation in mixed stochastic differential equations
In SimoneHermann/hierRegSDE:

Bayesian estimation of the parameters in the mixed SDE dY(t)= b(φ, t, Y(t))dt + γ s(t, Y(t)) dW(t).

1 2	estSDE_single(t, X, prior, start, bSDE, sVar, len = 1000, mod = "Gompertz", modVar = "")

`t`	vector of observation times
`X`	vector of the M trajectories
`prior`	list of prior parameters - list(mu, Omega, alpha, beta)
`start`	list of starting values
`sVar`	variance function
`len`	number of iterations of the MCMC algorithm
`mod`	model out of Gompertz, Richards, logistic, Weibull, only used instead of fODE
`modVar`	default value is sVar(t,x)=1, if "AR": sVar(t,x)=x
`fODE`	regression function
`propSd`	proposal standard deviation of phi is \|mu\|*propSd