Title: Convolution-Type Smoothed Quantile Regression
Authors@R: c(person("Xuming", "He", email = "xmhe@umich.edu", role = "aut"),
person("Xiaoou", "Pan", email = "xip024@ucsd.edu", role = c("aut", "cre")),
person("Kean Ming", "Tan", email = "keanming@umich.edu", role = "aut"),
Description: Fast and accurate convolution-type smoothed quantile regression. Implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Construct confidence intervals for regression coefficients using multiplier bootstrap.
Imports: Rcpp (>= 1.0.3), matrixStats, stats

Imports: Rcpp (>= 1.0.3), Matrix, matrixStats, stats

Description: 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 methods complemented with l_1-penalization and iteratively reweighted l_1-penalization are used to fit sparse models.
Imports: Rcpp (>= 1.0.3), Matrix, matrixStats, stats, caret

Description: 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.

Title: An R Front End for 'ACER ConQuest'
Authors@R: c(person("Dan", "Cloney", email = "dan.cloney@acer.org", role = c("cre", "aut")), person("Ray", "Adams", email = "ray.adams@acer.org", role = "aut"))
Extends 'ACER ConQuest' by allowing R users to call 'ACER ConQuest' from within R.
The user can also access 'ACER ConQuest' data objects by reading 'ACER ConQuest' System Files (generated by the 'ACER ConQuest' command 'put').
This is of particular use to those who are parsing text file output (e.g., 'show' files) as this is not consistent across releases of 'ACER ConQuest'.
Requires 'ACER ConQuest' version 4.29.3 or later. A demonstration version can be downloaded from <https://shop.acer.edu.au/acer-conquest-4>.
Packaged: 2019-02-22 06:49:37 UTC; acercloneyd