| CaDENCE-package {CaDENCE} | R Documentation |
A conditional density estimation network (CDEN) is a probabilistic extension of the standard multi-layer perceptron neural network (MLP) (Neuneier et al., 1994). A CDEN model allows users to estimate parameters of a specified probability density function conditioned upon values of a set of predictors using the MLP architecture. The result is a flexible model for the mean, the variance, exceedance probabilities, prediction intervals, etc. from the specified conditional distribution. Because the CDEN is based on the MLP, nonlinear relationships, including those involving complicated interactions between predictors, can be described by the modelling framework. The CaDENCE (Conditional Density Estimation Network Creation & Evaluation) package provides routines for creating and evaluating CDEN models in the R programming language.
| Package: | CaDENCE |
| Type: | Package |
| License: | GPL-2 |
Alex J. Cannon <http://www.eos.ubc.ca/~acannon>
Maintainer: Alex J. Cannon <acannon@eos.ubc.ca>
Cannon, A.J., 2012. Neural networks for probabilistic environmental prediction: Conditional Density Estimation Network Creation & Evaluation (CaDENCE) in R. Computers & Geosciences, 41: 126-135. doi:10.1016/j.cageo.2011.08.023
Neuneier, R., F. Hergert, W. Finnoff, and D. Ormoneit, 1994., Estimation of conditional densities: a comparison of neural network approaches. In: M. Marinaro and P. Morasso (eds.), Proceedings of ICANN 94, Berlin, Springer, p. 689-692.
CaDENCE_1.1.1.tar.gz
CaDENCE-manual.pdf
install.packages(pkgs="CaDENCE_1.1.1.tar.gz", type="source", repos=NULL)