Single-index quantile regression models are important tools in semiparametric regression to provide a comprehensive view of the conditional distributions of a response variable. This methods is especially useful when the data is heterogeneous or heavy tailed.
We provides functions that allow users to fit Single-Index Quantile Regression model via an efficient iterative local linear approach. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) at here for details.