quantreg5.98 package

Quantile Regression

print.KhmaladzeTest

Print a KhmaladzeTest object

print.rq

Print an rq object

print.summary.rq

Print Quantile Regression Summary Object

residuals.nlrq

Return residuals of an nlrq object

rq.fit.br

Quantile Regression Fitting by Exterior Point Methods

rq.fit

Function to choose method for Quantile Regression

rq.fit.scad

SCADPenalized Quantile Regression

rq.fit.sfn

Sparse Regression Quantile Fitting

akj

Density Estimation using Adaptive Kernel method

anova.rq

Anova function for quantile regression fits

bandwidth.rq

bandwidth selection for rq functions

boot.crq

Bootstrapping Censored Quantile Regression

boot.rq.pwxy

Preprocessing weighted bootstrap method

boot.rq.pxy

Preprocessing bootstrap method

boot.rq

Bootstrapping Quantile Regression

combos

Ordered Combinations

critval

Hotelling Critical Values

crq

Functions to fit censored quantile regression models

dither

Function to randomly perturb a vector

dynrq

Dynamic Linear Quantile Regression

FAQ

FAQ and ChangeLog of a package

KhmaladzeTest

Tests of Location and Location Scale Shift Hypotheses for Linear Model...

kuantile

Quicker Sample Quantiles

LassoLambdaHat

Lambda selection for QR lasso problems

latex

Make a latex version of an R object

latex.summary.rqs

Make a latex table from a table of rq results

latex.table

Writes a latex formatted table to a file

lm.fit.recursive

Recursive Least Squares

lprq

locally polynomial quantile regression

Munge

Munge rqss formula

nlrq.control

Set control parameters for nlrq

nlrq

Function to compute nonlinear quantile regression estimates

ParetoTest

Estimation and Inference on the Pareto Tail Exponent for Linear Models

plot.KhmaladzeTest

Plot a KhmaladzeTest object

plot.rq.process

plot the coordinates of the quantile regression process

plot.rqs

Visualizing sequences of quantile regressions

plot.rqss

Plot Method for rqss Objects

plot.summary.rqs

Visualizing sequences of quantile regression summaries

predict.rq

Quantile Regression Prediction

predict.rqss

Predict from fitted nonparametric quantile regression smoothing spline...

q489

Even Quicker Sample Quantiles

qrisk

Function to compute Choquet portfolio weights

qss

Additive Nonparametric Terms for rqss Fitting

QTECox

Function to obtain QTE from a Cox model

ranks

Quantile Regression Ranks

rearrange

Rearrangement

rq.fit.conquer

Optional Fitting Method for Quantile Regression

rq.fit.fnb

Quantile Regression Fitting via Interior Point Methods

rq.fit.fnc

Quantile Regression Fitting via Interior Point Methods

rq.fit.hogg

weighted quantile regression fitting

rq.fit.lasso

Lasso Penalized Quantile Regression

rq.fit.pfn

Preprocessing Algorithm for Quantile Regression

rq.fit.pfnb

Quantile Regression Fitting via Interior Point Methods

rq.fit.ppro

Preprocessing fitting method for QR

rq.fit.qfnb

Quantile Regression Fitting via Interior Point Methods

rq.fit.sfnc

Sparse Constrained Regression Quantile Fitting

rq.object

Linear Quantile Regression Object

rq.process.object

Linear Quantile Regression Process Object

rq

Quantile Regression

rq.wfit

Function to choose method for Weighted Quantile Regression

rqProcess

Compute Standardized Quantile Regression Process

rqs.fit

Function to fit multiple response quantile regression models

rqss.object

RQSS Objects and Summarization Thereof

rqss

Additive Quantile Regression Smoothing

sfn.control

Set Control Parameters for Sparse Fitting

srisk

Markowitz (Mean-Variance) Portfolio Optimization

summary.crq

Summary methods for Censored Quantile Regression

summary.rq

Summary methods for Quantile Regression

summary.rqss

Summary of rqss fit

table.rq

Table of Quantile Regression Results

Estimation and inference methods for models for conditional quantile functions: 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. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, <doi:10.1201/9781315120256>.