Title: Visualize Data for Host and Viral Population from 'SMITIDstruct'
Authors@R: person("Jean-Francois", "Rey", email = "jean-francois.rey@inra.fr", role = c("aut", "cre"))
Description: Visualisation tools for 'SMITIDstruct' package.
Allow to visualize host timeline, transmission tree, index diversities
and variant graph using HTMLwidgets. It mainly using D3JS javascript framework.

Authors@R: c(person("Jean-Francois", "Rey", email = "jean-francois.rey@inra.fr", role = c("aut", "cre")),
person("Julien", "Boge", email = "julien.boge.u@gmail.com", role = "ctb"))
Suggests: SMITIDstruct, knitr, shiny, testthat

Authors@R: c(person("Jean-Francois", "Rey", email = "jean-francois.rey@inrae.fr", role = c("aut", "cre")),
and variant graph using 'HTMLwidgets'. It mainly using 'D3JS' javascript framework.

Suggests: SMITIDstruct, knitr, shiny, testthat (>= 3.0.0)

Description: The SML package is a collection of statistical machine learning methods.

Title: Joint Feature Screening via Sparse MLE
Author: Qianxiang Zang,Chen Xu,Kelly Burkett
Imports: foreach, glmnet, mnormt, doParallel
Description: 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.

Description: 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 a user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.