SIS: Sure Independence Screening
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.
||R (≥ 3.1.1), glmnet, ncvreg, survival
||Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
||Diego Franco Saldana <diego at stat.columbia.edu>