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condmixt  

Conditional Density Estimation with Neural Network Conditional Mixtures
View on CRAN: Click here


Download and install condmixt package within the R console
Install from CRAN:
install.packages("condmixt")

Install from Github:
library("remotes")
install_github("cran/condmixt")

Install by package version:
library("remotes")
install_version("condmixt", "1.1")



Attach the package and use:
library("condmixt")
Maintained by
Julie Carreau
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-05-01
Latest Update: 2023-08-19
Description:
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.
How to cite:
Julie Carreau (2012). condmixt: Conditional Density Estimation with Neural Network Conditional Mixtures. R package version 1.1, https://cran.r-project.org/web/packages/condmixt. Accessed 30 Jan. 2025.
Previous versions and publish date:
1.0 (2012-05-01 17:17), 1.1 (2020-05-11 11:10)
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