Provides a Bayesian implementation of Nonparametric Factor Analysis. This implementation utilizes beta process priors on the factor loadings matrix to induce sparsity within the loadings matrix. This allows the number of orthogonal factors needed to be estimated and the posterior distribution uncovered. Allows mixed ordered margins in the manifest data (binary, ordered discrete, and continuous) and normalizes data using Gaussian copula.
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