fhmm: Fit a finite heirarchical mixture model
In apeterson91/fhmm:

Fits a finite heirarchical mixture model for comparison to the Nested Dirichlet Process the prior concentration parameters for both mixture components w and pi are fixed to be one.

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fhmm(r, n_j, mu_0 = 0, kappa_0 = 1, nu_0 = 1, sigma_0 = 1, L = 4,
  K = 4, iter_max, warm_up, thin = 1, include_warmup = FALSE,
  seed = NULL)

`r`	vector of distances associated with differing groups
`n_j`	matrix of integers denoting the start and length of each school's associated BEF distances
`mu_0`	mean hyperparameter for mu normal base measure. Default is 0; Normal(0,1).
`kappa_0`	variance hyperparameter for mu normal base measure. Default is 1; Normal(0,1).
`nu_0`	df hyperparameter for sigma inv chisq base measure. Default is 1; InvChisq(1,1);
`sigma_0`	scale hyperparameter for sigma inv chisq base measure. Default is 1; InvChisq(1,1);
`L`	component truncation number
`K`	intensity cluster truncation number
`iter_max`	total number of iterations for which to run sampler
`warm_up`	number of iterations for which to burn-in or "warm-up" sampler
`thin`	number of iterations to thin by
`seed`	integer with which to initialize random number generator