SMLE：一个用于超高维GLM联合特征筛选的R包
SMLE: An R Package for Joint Feature Screening in Ultrahigh-dimensional GLMs
摘要：
稀疏约束的极大似然估计（SMLE）在超高维回归的特征筛选中受到了广泛的关注。SMLE是一种计算方便的方法，它自然地将筛选过程中的特征之间的联合效应结合起来。我们开发了一个公开可用的R软件包SMLE，它提供了一个用户友好的环境进行SMLE在广义线性模型。特别是，该软件包包括使用SMLE进行SMLE筛选和筛选后选择的功能，该SMLE具有流行的选择标准，例如AIC和（扩展的）BIC。该软件包使用户能够灵活地控制一系列筛选参数，并可容纳数字和分类特征输入。该软件包的使用说明了广泛的数值例子，SMLE的良好表现。
英文摘要：
The sparsity-restricted maximum likelihood estimator (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features in the screening process. We develop a publicly available R package SMLE, which provides a user-friendly environment to carry out SMLE in generalized linear models. In particular, the package includes functions to conduct SMLE-screening and post-screening selection using SMLE with popular selection criteria such as AIC and (extended) BIC. The package gives users the flexibility in controlling a series of screening parameters and accommodates both numerical and categorical feature input. The usage of the package is illustrated on extensive numerical examples, where the promising performance of SMLE is well observed.
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