Markov chain Monte Carlo algorithms for non- and semi-parametric models: 1. Dirichlet process mixture models with function dpmj and 2. spike-slab variable selection in multivariate mean/variance regression models with function mvrm.
This work was partly supported by the Medical Research Council grant number G09018401.
| Package: | BNSP |
| Type: | Package |
| Version: | 2.1.0 |
| Date: | 2019-05-24 |
| License: | GPL (>=2) |
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Papageorgiou, G. and Marshall, B.C. (2019). Bayesian semiparametric analysis of multivariate continuous responses, with variable selection. arXiv:1905.08393 [stat.ME].
Papageorgiou, G. (2018). BNSP: an R Package for fitting Bayesian semiparametric regression models and variable selection. The R Journal, 10(2):526-548.
Papageorgiou, G. (2018). Bayesian density regression for discrete outcomes. arXiv:1603.09706v3 [stat.ME].
Papageorgiou, G., Richardson, S. and Best, N. (2015). Bayesian nonparametric models for spatially indexed data of mixed type. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 77:973-999.