Package: SFSI
Title: Sparse Family and Selection Index
Version: 1.2.0
Authors@R: c(
    person("Marco", "Lopez-Cruz", role = c("aut","cre"), email = "maraloc@gmail.com"),
    person("Gustavo", "de los Campos", role = c("aut"), email = "gustavoc@msu.edu"),
    person("Paulino", "Perez-Rodriguez", role = c("ctb"), email = "perpdgo@gmail.com"))
Date: 2022-08-16
Description: Here we provide tools for the estimation of coefficients in 
     penalized regressions when the (co)variance matrix of predictors 
     and the covariance vector between predictors and response, are 
     provided. These methods are extended to the context of a Selection Index
     (commonly used for breeding value prediction). The approaches offer 
     opportunities such as the integration of high-throughput traits in genetic 
     evaluations ('Lopez-Cruz et al., 2020') <doi:10.1038/s41598-020-65011-2> 
     and solutions for training set optimization in Genomic Prediction 
     ('Lopez-Cruz & de los Campos, 2021') <doi:10.1093/genetics/iyab030>.
LazyLoad: true
Depends: R (>= 3.5)
Imports: stats
Suggests: BGLR, Matrix, float, knitr, rmarkdown, ggplot2, parallel,
        reshape2, viridis, igraph
LinkingTo: float
VignetteBuilder: knitr
Encoding: UTF-8
License: GPL-3
NeedsCompilation: yes
Packaged: 2022-08-16 14:46:34 UTC; marco
Author: Marco Lopez-Cruz [aut, cre],
  Gustavo de los Campos [aut],
  Paulino Perez-Rodriguez [ctb]
Maintainer: Marco Lopez-Cruz <maraloc@gmail.com>
Repository: CRAN
Date/Publication: 2022-08-16 15:40:09 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 13:32:54 UTC; windows
Archs: i386, x64
