Depends: R (>= 3.1.1), glmnet, ncvreg, survival
Description: Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) and all of its variants in generalized linear models and the Cox proportional hazards model.

Author: Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao
Description: Variable selection techniques are essential tools for model
selection and estimation in high-dimensional statistical models. Through this
publicly available package, we provide a unified environment to carry out
variable selection using iterative sure independence screening (SIS) and all
of its variants in generalized linear models and the Cox proportional hazards

Authors@R: c(person("Yang", "Feng", email = "yang.feng@nyu.edu",
role = c("aut", "cre")),person("Jianqing", "Fan", email = "jqfan@princeton.edu",
role = c("aut")),person("Diego", "Franco Saldana", email = "diegofrasal@gmail.com",
role = c("aut")),person("Yichao", "Wu", email = "yichaowu@uic.edu",
role = c("aut")),person("Richard", "Samworth", email = "rjs57@hermes.cam.ac.uk",
variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).

Title: Sparse Interval Sliced Inverse Regression
person("Victor", "Picheny", , "victor.picheny@inra.fr", c("aut")),
person("Remi", "Servien", , "remi.servien@inrae.fr", c("aut")),
person("Nathalie", "Vialaneix", , "nathalie.vialaneix@inrae.fr", c("aut", "cre")))
Description: An interval fusion procedure for functional data in the
semiparametric framework of SIR, as described in
<doi:10.1007/s11222-018-9806-6>. Standard ridge and sparse SIR are