dot-fitMCMC: Fit the MIMOSA model via MCMC
In RGLab/MIMOSA: Mixture Models for Single-Cell Assays

This is an internal function that fits the MIMOSA model via MCMC. It is called from MIMOSA

.fitMCMC(
  data,
  inits = NULL,
  iter = 250000,
  burn = 50000,
  thin = 1,
  tune = 100,
  outfile = basename(tempfile(tmpdir = ".", fileext = ".dat")),
  alternative = "greater",
  UPPER = 0.5,
  LOWER = 0.15,
  FAST = TRUE,
  EXPRATE = 1e-04,
  pXi = c(1, 1),
  seed = 10
)

`data`	a `list` with elements names 'n.stim' and 'n.unstim', the stimulated and unstimulated counts. Must be at least of dimension 2.
`inits`	the initialization parameters for the MCMC routine. Can be initialized from `MDMix` with `initonly=TRUE`.
`iter`	the number of Mote Carlo iterations
`burn`	the number of burn-in iterations
`thin`	The thinning interval
`tune`	the number of iterations used for tuning the step size
`outfile`	the output file name
`alternative`	either 'greater' or 'not equal' for fitting the one-sided or two-sided MIMOSA model, respectively.
`UPPER`	tuning parameter for the upper bound on the acceptance ratio of each paramter
`LOWER`	tuning parmeter for the lower bound on the acceptance ratio of each paramter
`FAST`	`TRUE,FALSE`. Use the heuristic (FAST=TRUE) for fitting a one-sided model rather than recomputing the normalization constant via MCMC for each step. @importFrom coda mcmc
`EXPRATE`	the mean of the prior distribution for the model hyperparameters.
`pXi`	is the prior on the w, beta(1,1) by default).
`seed`	`numeric` random seed @rdname fitMCMC @name .fitMCMC @importFrom data.table fread