MixtureModel-class: An object for running MCMC simulations.
In CNPBayes: Bayesian mixture models for copy number polymorphisms

BatchModel and MarginalModel both inherit from this class.

k: An integer value specifying the number of latent classes.
hyperparams: An object of class 'Hyperparameters' used to specify the hyperparameters of the model.
theta: the means of each component and batch
sigma2: the variances of each component and batch
nu.0: the shape parameter for sigma2
sigma2.0: the rate parameter for sigma2
pi: mixture probabilities which are assumed to be the same for all batches
mu: overall mean
tau2: overall variance
data: the data for the simulation.
data.mean: the empirical means of the components
data.prec: the empirical precisions
z: latent variables
zfreq: table of latent variables
probz: n x k matrix of probabilities
u: chi-square draws for controlling t-distribution
logprior: log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))
loglik: log likelihood: ∑ p_k Φ(θ_k, σ_k)
mcmc.chains: an object of class 'McmcChains' to store MCMC samples
batch: an integer-vector numbering the different batches. Must the same length as data.
batchElements: a vector labeling from which batch each observation came from
modes: the values of parameters from the iteration which maximizes log likelihood and log prior
mcmc.params: An object of class 'McmcParams'
label_switch: length-one logical indicating problems with label switching
marginal_lik: the marginal likelihood of the model
.internal.constraint: Constraint on parameters. For internal use only.
.internal.counter: For internal use only.
marginal_lik: scalar for marginal likelihood