Title: ARMA-GARCH/APARCH models with GEV and stable distributions
Author: Thiago do Rego Sousa, Cira Etheowalda Guevara Otiniano and Silvia Regina Costa Lopes
Maintainer: Thiago do Rego Sousa <thiagoestatistico@gmail.com>
Description: Package for ARMA-GARCH or ARMA-APARCH modelling with GEV and stable conditional distributions.

Title: ARMA-GARCH/APARCH Models with GEV and Stable Distributions
Authors@R: c( person( "Thiago", "Sousa", role = c("aut", "cre"), email = "thiagoestatistico@gmail.com"), person( "Cira", "Otiniano", role = c("ctb"), email = "cira@unb.br"), person( "Silvia", "Lopes", role = c("ctb"), email = "silviarc.lopes@gmail.com"), person( "Diethelm", "Wuertz", role = c("ctb","cph"), email = "wuertz@itp.phys.ethz.ch"))
Description: Package for simulation and estimation of ARMA-GARCH/APARCH models with GEV and stable distributions.
Note: Package stable is available at http://www.robustanalysis.com

Title: GEV conditonal density estimation network
Maintainer: Alex J. Cannon <alex.cannon@ec.gc.ca>
Description: A flexible nonlinear modelling framework for nonstationary
generalized extreme value analysis in hydroclimatology

Title: GEV conditional density estimation network

Title: GEV Conditional Density Estimation Network
Description: Implements a flexible nonlinear modelling framework for nonstationary
generalized extreme value analysis in hydroclimatology.

Packaged: 2017-03-15 21:01:46 UTC; ECPACIFIC+cannona

Author: Alex J. Cannon [aut, cre] (<https://orcid.org/0000-0002-8025-3790>)
Description: Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
Packaged: 2020-04-23 21:33:21 UTC; ECPACIFIC+CannonA

Packaged: 2017-12-04 22:54:45 UTC; ECPACIFIC+cannona

Title: Misspecified Models for Gene-Environment Interaction
Description: Compute the bias in misspecified linear gene-environment interaction models. The most
generalized function for this objective is GE_bias().  However GE_bias() requires specification of many
higher order moments of covariates in the model.  If users are unsure about how to calculate/estimate
these higher order moments, it may be easier to use GE_bias_normal_squaredmis().  This function places
many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate
normal distribution) and is thus able to automatically calculate many of the higher order moments automatically,
necessitating only that the user specify some covariances.  There are also functions to solve for the bias
through simulation and non-linear equation solvers, these can be used to check your work.
Imports: mvtnorm, nleqslv, pracma, speedglm, rje, geepack, stats