DESCRIPTION

Package: ddsPLS
Version: 1.1.7
Date: 2020-03-23
Title: Data-Driven Sparse Partial Least Squares Robust to Missing Samples for Mono and Multi-Block Data Sets
Description: Allows to build Multi-Data-Driven Sparse Partial Least Squares models. Multi-blocks with 
    high-dimensional settings are particularly sensible to this. It comes with visualization
    functions and uses 'Rcpp' functions for fast computations and 'doParallel' to parallelize cross-validation.
    This is based on H Lorenzo, J Saracco, R Thiebaut (2019) <arXiv:1901.04380>.
    Many applications have been successfully realized.
    See <https://hadrienlorenzo.netlify.com/> for more information, documentation and examples.
Authors@R: c(person("Hadrien","Lorenzo", role = c("aut", "cre"),
                     email = "hadrien.lorenzo.2015@gmail.com"),
             person("Misbah","Razzaq", role = "ctb",
                     email = "misbah.razzaq@u-bordeaux.fr"),
             person("Jerome","Saracco", role = c("aut"),
                     email = "jerome.saracco@math.u-bordeaux1.fr"),
             person("Rodolphe","Thiebaut", role = c("aut"),
                     email = "rodolphe.thiebaut@u-bordeaux.fr"))
Maintainer: Hadrien Lorenzo <hadrien.lorenzo.2015@gmail.com>
License: MIT + file LICENSE
Encoding: UTF-8
ByteCompile: true
RoxygenNote: 7.1.0
Imports: RColorBrewer,MASS,graphics,stats,Rdpack,doParallel,foreach,parallel,corrplot,Rcpp (>= 0.12.18)
RdMacros: Rdpack
Suggests: knitr,rmarkdown,htmltools
VignetteBuilder: knitr
Depends: R (>= 2.10)
LinkingTo: Rcpp
URL: https://hadrienlorenzo.netlify.com/
download_url: https://github.com/hlorenzo/ddsPLS/archive/v116.zip