SIS

R package SIS: 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.

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References in zbMATH (referenced in 5 articles , 1 standard article )

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  1. Diego Saldana; Yang Feng: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models (2018) not zbMATH
  2. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  3. Razieh Nabi Abdolyousefi, Xiaogang Su: coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models (2016) arXiv
  4. Zhong, Wei; Zhu, Liping: An iterative approach to distance correlation-based sure independence screening (2015)
  5. Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)