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

A Way of Writing Functions that Quote their Arguments
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.

Jairo Diaz, Sylvain Sardy, Caroline Giacobino, Nick Hengartner.
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.

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

Selection of a threshold parameter based on the Quantile Universal Threshold (QUT) for GLM-lasso and Square-root lasso to obtain a sparse model
with a good compromise between high true positive rate and low false discovery rate.

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
with a good compromise between high true positive rate and low false discovery rate.

Jairo Diaz-Rodriguez, Sylvain Sardy, Caroline Giacobino, Nick Hengartner.

c(person("Jairo", "Diaz-Rodriguez", role = c("aut", "cre","cph"),
email = "adjairo@uninorte.edu.co"),
person("Sylvain", "Sardy", role = c("aut", "ths"),
email = "sylvain.sardy@unige.ch"),
person("Caroline", "Giacobino", role = c("aut")),
person("Nick", "Hengartner", role = c("aut")))
Thresholding based tests for null hypothesis of the form A beta =c, and the Quantile Universal Threshold (QUT) for lasso regularization of Generalized Linear Models (GLM) and square-root lasso to obtain a sparse model with a good compromise between high true positive rate and low false discovery rate. Giacobino et al. (2017) <doi:10.1214/17-EJS1366>. Sardy et al. (2017) <arXiv:1708.02908>.
Jairo Diaz-Rodriguez [aut, cre, cph],
Sylvain Sardy [aut, ths],
Caroline Giacobino [aut],
Nick Hengartner [aut]