TY  - JA
AU  - Perez-Rodriguez,P.
AU  - Gianola,D.
AU  - Rosa,G.J.M.
AU  - Weigel,K.A.
AU  - Crossa,J.
TI  - Technical Note:  An R package for fitting Bayesian regularized neural networks with applications in animal breeding
SN  - 1525-3163
PY  - 2013///
KW  - Animal model
KW  - BRNN
KW  - dominance and additive effects
KW  - Genomic selection
KW  - non-parametric models
N1  - Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0021-8812
N2  - In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees or both. These models include, among others, the Bayesian Regularized Neural Networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R brnn package described here implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples
T2  - Journal of Animal Science
DO  - https://doi.org/10.2527/jas.2012-6162
ER  -