Visualize Data for Host and Viral Population from 'SMITIDstruct'
using 'HTMLwidgets'
c(person("Jean-Francois", "Rey", email = "jean-francois.rey@inrae.fr", role = c("aut", "cre")),
person("Julien", "Boge", email = "julien.boge.u@gmail.com", role = "ctb"))
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.

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

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

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.

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.

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.
stats, graphics, utils, matrixcalc, mvnfast