Title: Quantile Regression Outlier Diagnostics with K Left Out Analysis
Author: Wenjing Wang <wenjingwangr@gmail.com>, Di Cook <visnut@gmail.com>, Earo Wang <earo.wang@gmail.com>
Description: Diagnostics methods for quantile regression models for detecting influential observations:
robust distance methods for general quantile regression models; generalized Cook's distance and
Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference
of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>;
mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model.
Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.
Imports: stats, quantreg, purrr, magrittr, ALDqr, bayesQR, MCMCpack,
ggplot2, knitr, gridExtra, GIGrvg, dplyr, tidyr, robustbase,

Title: Get 'Quora' Ads Data via the 'Windsor.ai' API
Description: Collect  your data on digital marketing campaigns from 'Quora' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Title: A Way of Writing Functions that Quote their Arguments
Description: A facility for writing functions that quote their arguments,
may sometimes evaluate them in the environment where they were quoted,
and may pass them as quoted to other functions.

Author: Jairo Diaz, Sylvain Sardy, Caroline Giacobino, Nick Hengartner.
Description: Selection of a threshold parameter for GLM-lasso to obtain a sparse model
with a good compromise between high true positive rate and low false discovery rate.

Author: Jairo Diaz Rodriguez, Sylvain Sardy, Caroline Giacobino, Nick Hengartner.
Description: Selection of a threshold parameter for GLM-lasso and Square-root lasso to obtain a sparse model

Description: Selection of a threshold parameter based on the Quantile Universal Threshold (QUT) for GLM-lasso and Square-root lasso to obtain a sparse model

Description: Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for GLM-lasso and Square-root lasso to obtain a sparse model