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condmixt: Conditional Density Estimation with Neural Network Conditional Mixtures
Neural network conditional mixtures are mixture models whose parameters are predicted by a neural network. The mixture model can thus change its parameters in response to changes in predictive covariates. Mixtures included are gaussian, log-normal and hybrid Pareto mixtures. The latter relies on the generalized Pareto distribution to account for the presence of large extreme events. The unconditional mixtures are also available.
Getting started
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Package details |
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| Author | Julie Carreau |
| Maintainer | Julie Carreau <julie.carreau@ird.fr> |
| License | GPL-2 |
| Version | 1.1 |
| Package repository | View on CRAN |
| Installation |
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