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:	1.4.1
Depends:	R (≥ 3.6.0)
Imports:	stats, scales, tensorEVD (≥ 0.1.3), parallel, reshape2, viridis, igraph, stringr, ggplot2
Suggests:	BGLR, knitr, rmarkdown
Published:	2024-08-23
DOI:	10.32614/CRAN.package.SFSI
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
URL:	https://github.com/MarcooLopez/SFSI
NeedsCompilation:	yes
Citation:	SFSI citation info
Materials:	NEWS
CRAN checks:	SFSI results

Documentation:

Reference manual:	SFSI.pdf
Vignettes:	Documentation: Lopez-Cruz and de los Campos (2021) Genetics 218(1):1-10 (source, R code) Documentation: Lopez-Cruz et. al. (2020) Sci. Rep. 10:8195) (source, R code)

Downloads:

Package source:	SFSI_1.4.1.tar.gz
Windows binaries:	r-devel: SFSI_1.4.1.zip, r-release: SFSI_1.4.1.zip, r-oldrel: SFSI_1.4.1.zip
macOS binaries:	r-release (arm64): SFSI_1.4.1.tgz, r-oldrel (arm64): SFSI_1.4.1.tgz, r-release (x86_64): SFSI_1.4.1.tgz, r-oldrel (x86_64): SFSI_1.4.1.tgz
Old sources:	SFSI archive

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