| GSelection-package {GSelection} | R Documentation |
Genomic Selection
Description
Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.
Details
The DESCRIPTION file:
| Package: | GSelection |
| Type: | Package |
| Title: | Genomic Selection |
| Version: | 0.1.0 |
| Author: | Sayanti Guha Majumdar, Anil Rai, Dwijesh Chandra Mishra |
| Maintainer: | Sayanti Guha Majumdar <sayanti23gm@gmail.com> |
| Description: | Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | SAM, penalized, gdata, stats, utils |
| RoxygenNote: | 6.1.1 |
| Depends: | R (>= 3.5) |
| NeedsCompilation: | no |
| Packaged: | 2019-10-26 10:25:25 UTC; user6 |
Index of help topics:
GS Genotypic and phenotypic simulated dataset GSelection-package Genomic Selection RED Redundancy Rate feature.selection Genomic Feature Selection genomic.prediction Genomic Prediction hsic.var.ensemble Error Variance Estimation in Genomic Prediction hsic.var.rcv Error Variance Estimation in Genomic Prediction spam.var.ensemble Error Variance Estimation in Genomic Prediction spam.var.rcv Error Variance Estimation in Genomic Prediction
Author(s)
Sayanti Guha Majumdar, Anil Rai, Dwijesh Chandra Mishra
Maintainer: Sayanti Guha Majumdar <sayanti23gm@gmail.com>
References
Guha Majumdar, S., Rai, A. and Mishra, D. C. (2019). Integrated framework for selection of additive and non-additive genetic markers for genomic selection. Journal of Computational Biology. doi:10.1089/cmb.2019.0223
Ravikumar, P., Lafferty, J., Liu, H. and Wasserman, L. (2009). Sparse additive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(5), 1009-1030. doi:10.1111/j.1467-9868.2009.00718.x
Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P. and Sugiyama, M. (2014). High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Computation, 26(1):185-207. doi:10.1162/NECO_a_00537