Bayesian nonparametric density estimation
This package performs Bayesian nonparametric density estimation via a normalized random measure mixture model. The package allows the user to specify the mixture kernel, the mixing normalized measure and the choice of performing fully nonparametric mixtures on locations and scales, or semiparametric mixtures on locations only with common scale parameter. Options for the kernels are: two kernels with support in the real line (gaussian and double exponential), two more kernels in the positive line (gamma and lognormal) and one with bounded support (beta). The options for the normalized random measures are members of the class of normalized generalized gamma, which include the Dirichlet process, the normalized inversed gaussian process and the normalized stable process.
|License:||GPL version 2 or later|
The package includes two main functions: MixNRMI1 and MixNRMI2, which implement semiparametric mixtures and fully nonparametric mixtures, respectively. Additionally, the package includes several other functions required for sampling from conditional distributions in the MCMC implementation. These functions intended for internal use only.
Barrios, E., Lijoi, A., Nieto-Barajas, L.E. and Prüenster, I. Maintainer: <firstname.lastname@example.org> Ernesto Barrios
Barrios, E., Lijoi, A., Nieto-Barajas, L. E. and Prüenster, I. (2013). Modeling with Normalized Random Measure Mixture Models. Statistical Science. Vol. 28, No. 3, 313-334.
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