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. See Koenker (2006) <doi:10.1017/CBO9780511754098> and
Koenker et al (2017) <doi:10.1201/9781315120256>.

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. See Koenker (2006) <doi:10.1017/CBO9780511754098> and
Koenker et al. (2017) <doi:10.1201/9781315120256>.

tripack, akima, MASS, survival, rgl, logspline, nor1mix,
Formula, zoo, R.rsp, conquer

methods, graphics, Matrix, MatrixModels, survival, MASS
tripack, akima, rgl, logspline, nor1mix, Formula, zoo, R.rsp,
conquer

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>.
tripack, interp, rgl, logspline, nor1mix, Formula, zoo,
R.rsp, conquer

Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov, Ivan Fernandez-Val
R (>= 2.10), quantreg, mnormt, fda, Rearrangement
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.