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.

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

Downloads:

Reference manual: condmixt.pdf
Package source: condmixt_1.1.tar.gz
Windows binaries: r-devel: condmixt_1.1.zip, r-devel-UCRT: condmixt_1.1.zip, r-release: condmixt_1.1.zip, r-oldrel: condmixt_1.1.zip
macOS binaries: r-release (arm64): condmixt_1.1.tgz, r-release (x86_64): condmixt_1.1.tgz, r-oldrel: condmixt_1.1.tgz
Old sources: condmixt archive

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