quantreg v5.35

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by Roger Koenker View Source

Quantile Regression

Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included.

Functions in quantreg

Name	Description
bandwidth.rq	bandwidth selection for rq functions
kuantile	Quicker Sample Quantiles
Bosco	Boscovich Data
latex	Make a latex version of an R object
rq.fit.pfn	Preprocessing Algorithm for Quantile Regression
CobarOre	Cobar Ore data
FAQ	FAQ and ChangeLog of a package
rqss.object	RQSS Objects and Summarization Thereof
Mammals	Garland(1983) Data on Running Speed of Mammals
dynrq	Dynamic Linear Quantile Regression
rq.fit.scad	SCADPenalized Quantile Regression
plot.rq	plot the coordinates of the quantile regression process
critval	Hotelling Critical Values
combos	Ordered Combinations
srisk	Markowitz (Mean-Variance) Portfolio Optimization
plot.rqs	Visualizing sequences of quantile regressions
sfn.control	Set Control Parameters for Sparse Fitting
print.rq	Print an rq object
print.summary.rq	Print Quantile Regression Summary Object
ranks	Quantile Regression Ranks
summary.crq	Summary methods for Censored Quantile Regression
engel	Engel Data
QTECox	Function to obtain QTE from a Cox model
Peirce	C.S. Peirce's Auditory Response Data
boot.crq	Bootstrapping Censored Quantile Regression
boot.rq	Bootstrapping Quantile Regression
latex.table	Writes a latex formatted table to a file
latex.summary.rqs	Make a latex table from a table of rq results
predict.rq	Quantile Regression Prediction
predict.rqss	Predict from fitted nonparametric quantile regression smoothing spline models
rq.fit	Function to choose method for Quantile Regression
rq.fit.br	Quantile Regression Fitting by Exterior Point Methods
rq.object	Linear Quantile Regression Object
rearrange	Rearrangement
rq.process.object	Linear Quantile Regression Process Object
summary.rq	Summary methods for Quantile Regression
lm.fit.recursive	Recursive Least Squares
summary.rqss	Summary of rqss fit
lprq	locally polynomial quantile regression
residuals.nlrq	Return residuals of an nlrq object
qrisk	Function to compute Choquet portfolio weights
qss	Additive Nonparametric Terms for rqss Fitting
rq.fit.hogg	weighted quantile regression fitting
rq	Quantile Regression
rq.fit.lasso	Lasso Penalized Quantile Regression
rq.fit.fnb	Quantile Regression Fitting via Interior Point Methods
rq.fit.fnc	Quantile Regression Fitting via Interior Point Methods
rqs.fit	Function to fit multiple response quantile regression models
rq.fit.sfn	Sparse Regression Quantile Fitting
rq.fit.sfnc	Sparse Constrained Regression Quantile Fitting
rqss	Additive Quantile Regression Smoothing
rq.wfit	Function to choose method for Weighted Quantile Regression
rqProcess	Compute Standardized Quantile Regression Process
table.rq	Table of Quantile Regression Results
uis	UIS Drug Treatment study data
barro	Barro Data
crq	Functions to fit censored quantile regression models
akj	Density Estimation using Adaptive Kernel method
dither	Function to randomly perturb a vector
nlrq	Function to compute nonlinear quantile regression estimates
nlrq.control	Set control parameters for nlrq
plot.summary.rqs	Visualizing sequences of quantile regression summaries
anova.rq	Anova function for quantile regression fits
plot.rqss	Plot Method for rqss Objects
KhmaladzeTest	Tests of Location and Location Scale Shift Hypotheses for Linear Models
gasprice	Time Series of US Gasoline Prices
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Vignettes of quantreg

Name
abbrev.bib
book.bib
gasprice.R
rq.Rnw
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Details

Repository	CRAN
License	GPL (>= 2)
URL	https://www.r-project.org
NeedsCompilation	yes
Packaged	2018-02-02 17:59:45 UTC; roger
Date/Publication	2018-02-02 21:20:14 UTC

suggests	akima , Formula , logspline , MASS , nor1mix , rgl , survival , tripack , zoo
imports	graphics , Matrix , MatrixModels , methods
depends	R (>= 2.6) , SparseM , stats
Contributors	Brian Ripley, Philippe Grosjean, Pin Ng, Stephen Portnoy, Achim Zeileis