Package: conquer 1.3.3

conquer: Convolution-Type Smoothed Quantile Regression

Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.

Authors:Xuming He [aut], Xiaoou Pan [aut, cre], Kean Ming Tan [aut], Wen-Xin Zhou [aut]

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Bug tracker:https://github.com/xiaooupan/conquer/issues

On CRAN:

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Last updated 2 years agofrom:3adb73cfa7. Checks:OK: 9. Indexed: yes.

Exports:conquerconquer.cv.regconquer.processconquer.reg

Dependencies:latticeMatrixmatrixStatsRcppRcppArmadillo