SFSI: Sparse Family and Selection Index

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>.

Version: 1.3.1
Depends: R (≥ 3.6.0), ggplot2
Imports: stats, scales, tensorEVD, parallel, reshape2, viridis, igraph
Suggests: BGLR, knitr, rmarkdown
Published: 2023-11-18
Author: Marco Lopez-Cruz [aut, cre], Gustavo de los Campos [aut], Paulino Perez-Rodriguez [ctb]
Maintainer: Marco Lopez-Cruz <maraloc at gmail.com>
License: GPL-3
NeedsCompilation: yes
Citation: SFSI citation info
Materials: NEWS
CRAN checks: SFSI results


Reference manual: SFSI.pdf
Vignettes: Documentation: Regularized selection indices for breeding value prediction using hyper-spectral image data (Lopez-Cruz et. al., 2020, Sci Rep. 10:8195)
Documentation: Optimal breeding value prediction using a Sparse Selection Index (Lopez-Cruz and de los Campos, 2020, Genetics)


Package source: SFSI_1.3.1.tar.gz
Windows binaries: r-devel: SFSI_1.3.1.zip, r-release: SFSI_1.3.1.zip, r-oldrel: SFSI_1.3.1.zip
macOS binaries: r-release (arm64): SFSI_1.3.1.tgz, r-oldrel (arm64): SFSI_1.3.1.tgz, r-release (x86_64): SFSI_1.3.1.tgz, r-oldrel (x86_64): SFSI_1.2.0.tgz
Old sources: SFSI archive


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