Joint Feature Screening via Sparse MLE R package SMLE version 2.2-3 Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025)<doi:10.18637/jss.v115.i08>. Software Depends: R(>= 4.0.0) Imports: glmnet, matrixcalc, mvnfast Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Qianxiang Zang <SMLEmaintainer@gmail.com> Comprehensive R Archive Network (CRAN) Qianxiang Zang [aut, cre], Chen Xu [aut], Kelly Burkett [aut] GPL-3 2026-01-18 application/tgz https://CRAN.R-project.org/package=SMLE doi:10.32614/CRAN.package.SMLE