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.

Keywords for this software

grey-box optimization
minimax concave penalty
negative loglikelihood
algorithm
iterative soft thresholding
variable selection
convex optimization
derivative-free optimization
regression models
general constraints
lasso penalty
expectation and maximization (EM) algorithm
goodness-of-fit
smoothly clipped absolute deviation penalty
non-convex optimization
nonlinear programming
surrogate modeling

References in zbMATH (referenced in 3 articles )

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Sorted by year (citations)

Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
Razieh Nabi Abdolyousefi, Xiaogang Su: coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models (2016) arXiv
Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)