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.
| Version: | 1.1 |
| Depends: | evd |
| Published: | 2020-05-11 |
| Author: | Julie Carreau |
| Maintainer: | Julie Carreau <julie.carreau at ird.fr> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | condmixt results |
| Reference manual: | condmixt.pdf |
| Package source: | condmixt_1.1.tar.gz |
| Windows binaries: | r-devel: condmixt_1.1.zip, r-release: condmixt_1.1.zip, r-oldrel: condmixt_1.1.zip |
| macOS binaries: | r-release: condmixt_1.1.tgz, r-oldrel: condmixt_1.1.tgz |
| Old sources: | condmixt archive |
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