C++ function to estimate DDP models with 1 grouping variables
a vector of observations.
group allocation of the data.
number of groups.
vector to evaluate the density.
number of iterations.
number of burn-in iterations.
expectation of location component.
tuning parameter of variance of location component.
parameter of scale component.
parameter of scale component.
mass of Dirichlet process.
prior weight of the specific processes.
tuning parameter of weights distribution
number of approximating values.
number of approximating values of the importance step for the weights updating.
number of iterations to show current updating.
if TRUE, return also the estimated density (default TRUE).
print the status.
if TRUE return only the posterior mean of the density