SMLE v0.3.1


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Joint Feature Screening via Sparse MLE

Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Sparse Maximal Likelihood Estimator (SMLE) (Xu and Chen (2014)<doi:10.1080/01621459.2013.879531>) provides an efficient implementation for the joint feature screening method on high-dimensional generalized linear models. It also conducts a post-screening selection based on user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.

Functions in SMLE

Name Description
print.selection Print a selection object from smle_select
plot.smle Plots to visualize the SMLE screening step
print.sdata Print function for simulated data
Gen_Data Data simulator for high-dimensional GLMs
smle-package Joint SMLE-screening for generalized linear models
print.smle Print a SMLE object from SMLE
smle_select Elaborative feature selection with SMLE
SMLE Joint feature screening via sparse maximum likelihood estimation for GLMs
plot.selection Plots to visualize the selection steps
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License GPL-2
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
NeedsCompilation no
Repository CRAN
Packaged 2020-05-13 04:44:44 UTC; mac
Date/Publication 2020-05-18 15:40:03 UTC

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