The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. 
    The model was based on the evaluation of local alternatives, representing 
    regional potentialities, so as to fulfill demands of economic projects. 
    After defining demand profiles in terms of their technological coefficients, 
    the degree of importance of factors is defined so as to represent  
    the productive activity. The method can detect a surplus of supply without 
    the restriction of the distance of classical algebra, defining a hierarchy 
    of location alternatives. In COPPE-Cosenza Model, the distance between 
    factors is measured in terms of the difference between grades of memberships
    of the same factors belonging to two or more  sets under comparison. The 
    required factors are classified under the following linguistic variables: 
    Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant 
    (I). And the alternatives can assume the following linguistic variables: 
    Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and 
    Inexistent (In). The model also provides flexibility, allowing different 
    aggregation rules to be performed and defined by the Decision Maker. Such 
    feature is considered in this package, allowing the user to define other 
    aggregation matrices, since it considers the same linguistic variables 
    mentioned.

Performs Bayesian nonparametric density estimation using Martingale
 posterior distributions including the Copula Resampling (CopRe) algorithm. 
 Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and
 an extension to include full uncertainty quantification via a predictive 
 sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate 
 random nonparametric distributions as output, leading to complete nonparametric 
 inference on posterior summaries. Routines for calculating arbitrary 
 functionals from the sampled distributions are included as well as an important 
 algorithm for finding the number and location of modes, which can then be used 
 to estimate the clusters in the data using, for example, k-means.
 Implements work developed in Moya B., Walker S. G. (2022).
 <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) 
 <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) 
 <doi:10.1080/01621459.1995.10476550>.