GEVcdn: An R package for nonstationary extreme value analysis by generalized extreme value conditional density estimation network

An R package is developed for the Generalized Extreme Value conditional density estimation network (GEVcdn). Parameters in a GEV distribution are specified as a function of covariates using a probabilistic variant of the multilayer perceptron neural network. If the covariate is time or is dependent on time, then the GEVcdn model can be used to perform nonlinear, nonstationary extreme value analysis. Due to the flexibility of the neural network architecture, the model is capable of representing a wide range of nonstationary relationships, including those involving interactions between covariates. Model parameters are estimated by generalized maximum likelihood, an approach that is tailored to the analysis of hydroclimatological extremes. Functions are included to assist in the calculation of parameter uncertainty via bootstrapping.