StartNewsamples: Start new model fits
In ggdmc: Cognitive Models

Fit a hierarchical or a fixed-effect model, using Bayeisan optimisation. We use a specific type of pMCMC algorithm, the DE-MCMC. This particular sampling method includes crossover and two different migration operators. The migration operators are similar to random-walk algorithm. They wouold be less efficient to find the target parameter space, if been used alone.

StartNewsamples(data, prior = NULL, nmc = 200, thin = 1,
  nchain = NULL, report = 100, rp = 0.001, gammamult = 2.38,
  pm0 = 0.05, pm1 = 0.05, block = TRUE, ncore = 1)

run(samples, nmc = 500, thin = 1, report = 100, rp = 0.001,
  gammamult = 2.38, pm0 = 0, pm1 = 0, block = TRUE, ncore = 1,
  add = FALSE)

`data`	data model instance(s)
`prior`	prior objects. For hierarchical model, this must be a list with three sets of prior distributions. Each is respectively named, "pprior", "location", and "scale".
`nmc`	number of Monte Carlo samples
`thin`	thinning length
`nchain`	number of chains
`report`	progress report interval
`rp`	tuning parameter 1
`gammamult`	tuning parameter 2. This is the step size.
`pm0`	probability of migration type 0 (Hu & Tsui, 2010)
`pm1`	probability of migration type 1 (Turner et al., 2013)
`block`	Only for hierarchical modeling. A Boolean switch for update one parameter at a time
`ncore`	Only for non-hierarchical, fixed-effect models with many subjects.
`samples`	posterior samples.
`add`	Boolean whether to add new samples