FFT.run: Generate synthetic data from simple BSD process and infer...
In jasonxu90/bdsem: Analysis of discretely observed Birth-Death-Shift processes

The main function for simulation studies assessing our generating function approach in the the discretely observed simple birth-shift-death-process without covariates. Generates synthetic datasets using makedata.simple, and infers the MLE rates for each using FFT.optim.

1 2	FFT.run(N, tList, lam, v, mu, initList, initGuess, s1.seq, s2.seq)

`N`	An integer specifying the number of observation intervals/realization from the simple BSD process.
`tList`	A list of observation interval lengths. The number of datasets returned is equal to the length of tList
`lam`	Per-particle birth rate
`v`	Per-particle shift rate
`mu`	Per-particle death rate
`initList`	A vector containing possible initial population sizes
`initGuess`	A vector containing the initial guess of birth, shift, and death rates
`s1.seq`	A vector of complex arguments evenly spaced along the unit circle
`s2.seq`	A vector of complex arguments evenly spaced along the unit circle