EXRQ: Extreme Regression of Quantiles
Version 1.0

Estimation for high conditional quantiles based on quantile regression.

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AuthorHuixia Judy Wang
Date of publication2016-07-06 23:48:41
MaintainerHuixia Judy Wang <judywang@gwu.edu>
LicenseGPL-3
Version1.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("EXRQ")

Man pages

Estc.func: Estimation of the C vector
est.gamma.func: Estimation of the Extreme Value Index on the Original Scale
EVI.CFG.func: Hill Estimator of the Extreme Value Index
PowT.1tau.func: Estimation for Quantile Power Transformation Model
qpareto: Quantile of the Pareto Distribution
rpareto: Random Generation for the Pareto Distribution
select.k.func: Selection of the Tuning Parameter k
testC.EVI: Testing the Constancy of EVI Over Covariates
ThreeStage: Three-Stage Extreme Conditional Quantile Estimator
TwoStage: Two-Stage Extreme Conditional Quantile Estimator

Functions

EVI.CFG.func Man page Source code
Estc.func Man page Source code
PowT.1tau.func Man page Source code
ThreeStage Man page Source code
TwoStage Man page Source code
est.gamma.func Man page Source code
qpareto Man page Source code
rpareto Man page Source code
select.k.func Man page Source code
testC.EVI Man page Source code

Files

NAMESPACE
R
R/EXRQ.R
MD5
DESCRIPTION
man
man/PowT.1tau.func.Rd
man/testC.EVI.Rd
man/qpareto.Rd
man/rpareto.Rd
man/est.gamma.func.Rd
man/select.k.func.Rd
man/Estc.func.Rd
man/TwoStage.Rd
man/ThreeStage.Rd
man/EVI.CFG.func.Rd
EXRQ documentation built on May 19, 2017, 7:23 a.m.