Can be used to install and download massive bioinformatics analysis softwares and databases, such as NGS reads mapping tools with its required databases.

Title: Integrator of Bioinformatics Resources
Authors@R: person("Jianfeng", "Li", email = "lee_jianfeng@sjtu.edu.cn", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2349-208X"))
Can be used to integrate massive bioinformatics resources, such as tool/script and database. It provides the R functions and Shiny web application. Hundreds of bioinformatics tool/script and database have been included.
Suggests: knitr, rmarkdown, testthat, prettydoc, DT
Author: Jianfeng Li [aut, cre] (<https://orcid.org/0000-0003-2349-208X>)

Title: Find biomarkers in two-class discrimination problems
Description: The package implements PCLDA, PLSDA, and several t-tests
to find those variables that allow for adequate discrimination
between two classes. Apart from the usual approaches based on
the size of model coefficients, a set of functions is provided
that assess the stability of the potential biomarkers under

Description: The package implements PCLDA, PLSDA, and several t-tests to find those variables that allow for adequate discrimination between two classes. Apart from the usual approaches based on the size of model coefficients, a set of functions is provided that assess the stability of the potential biomarkers under crossvalidation.

Description: The package provides biomarker selection for several
classification methods: the lasso/elastic net, PCLDA, PLSDA,
and several t-tests. Two approaches are provided, one based on
the stability of model coefficients under perturbation, and the

Description: The package provides biomarker selection for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches are provided, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Depends: pls, glmnet, MASS, st (>= 1.1.6)

Title: Find Biomarkers in Two-Class Discrimination Problems
Description: Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism.