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quantreg (version 5.61)

Quantile Regression

Description

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 now included.

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Version

Install

install.packages('quantreg')

Monthly Downloads

343,143

Version

5.61

License

GPL (>= 2)

Maintainer

Roger Koenker

Last Published

July 9th, 2020

Functions in quantreg (5.61)

Bosco

Boscovich Data
FAQ

FAQ and ChangeLog of a package
KhmaladzeTest

Tests of Location and Location Scale Shift Hypotheses for Linear Models
Peirce

C.S. Peirce's Auditory Response Data
Mammals

Garland(1983) Data on Running Speed of Mammals
QTECox

Function to obtain QTE from a Cox model
CobarOre

Cobar Ore data
bandwidth.rq

bandwidth selection for rq functions
akj

Density Estimation using Adaptive Kernel method
anova.rq

Anova function for quantile regression fits
barro

Barro Data
crq

Functions to fit censored quantile regression models
boot.crq

Bootstrapping Censored Quantile Regression
nlrq.control

Set control parameters for nlrq
dither

Function to randomly perturb a vector
latex.summary.rqs

Make a latex table from a table of rq results
latex

Make a latex version of an R object
lprq

locally polynomial quantile regression
combos

Ordered Combinations
critval

Hotelling Critical Values
plot.KhmaladzeTest

Plot a KhmaladzeTest object
nlrq

Function to compute nonlinear quantile regression estimates
plot.rq

plot the coordinates of the quantile regression process
print.KhmaladzeTest

Print a KhmaladzeTest object
plot.rqss

Plot Method for rqss Objects
rq.fit.fnb

Quantile Regression Fitting via Interior Point Methods
plot.summary.rqs

Visualizing sequences of quantile regression summaries
boot.rq

Bootstrapping Quantile Regression
latex.table

Writes a latex formatted table to a file
lm.fit.recursive

Recursive Least Squares
print.rq

Print an rq object
gasprice

Time Series of US Gasoline Prices
rq.fit.fnc

Quantile Regression Fitting via Interior Point Methods
predict.rq

Quantile Regression Prediction
predict.rqss

Predict from fitted nonparametric quantile regression smoothing spline models
plot.rqs

Visualizing sequences of quantile regressions
boot.rq.pxy

Preprocessing bootstrap method
dynrq

Dynamic Linear Quantile Regression
ranks

Quantile Regression Ranks
qss

Additive Nonparametric Terms for rqss Fitting
rearrange

Rearrangement
rq.fit.scad

SCADPenalized Quantile Regression
rq.fit

Function to choose method for Quantile Regression
rq

Quantile Regression
rq.fit.sfn

Sparse Regression Quantile Fitting
kuantile

Quicker Sample Quantiles
rq.fit.sfnc

Sparse Constrained Regression Quantile Fitting
engel

Engel Data
residuals.nlrq

Return residuals of an nlrq object
rq.object

Linear Quantile Regression Object
rq.fit.pfn

Preprocessing Algorithm for Quantile Regression
qrisk

Function to compute Choquet portfolio weights
table.rq

Table of Quantile Regression Results
print.summary.rq

Print Quantile Regression Summary Object
summary.rqss

Summary of rqss fit
rq.fit.conquer

Optional Fitting Method for Quantile Regression
rq.fit.hogg

weighted quantile regression fitting
sfn.control

Set Control Parameters for Sparse Fitting
rq.fit.ppro

Preprocessing fitting method for QR
rq.fit.br

Quantile Regression Fitting by Exterior Point Methods
rqss

Additive Quantile Regression Smoothing
rq.wfit

Function to choose method for Weighted Quantile Regression
rq.process.object

Linear Quantile Regression Process Object
rq.fit.lasso

Lasso Penalized Quantile Regression
srisk

Markowitz (Mean-Variance) Portfolio Optimization
rqs.fit

Function to fit multiple response quantile regression models
rqProcess

Compute Standardized Quantile Regression Process
summary.crq

Summary methods for Censored Quantile Regression
summary.rq

Summary methods for Quantile Regression
uis

UIS Drug Treatment study data
rqss.object

RQSS Objects and Summarization Thereof