VIGoR: Variational Bayesian Inference for Genome-Wide Regression
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
| Version: |
1.1.5 |
| Published: |
2024-09-11 |
| DOI: |
10.32614/CRAN.package.VIGoR |
| Author: |
Akio Onogi [aut, cre, cph],
Hiroyoshi Iwata [cph],
Takuji Nishimura [ctb] (Developer of Mersenne twister in header1.h),
Makoto Matsumoto [ctb] (Developer of Mersenne twister in header1.h),
STRUCTURE software contributors [ctb] (Provide snorm and RNormal
functions in header2.h),
Alan Miller [ctb] (Program mylgamma function in header2.h),
Peter Beerli [ctb] (Translate mylgamma function in header2.h) |
| Maintainer: |
Akio Onogi <onogiakio at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
yes |
| CRAN checks: |
VIGoR results |
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