- Unique identifier:
- Software type:
- Output data:
- A list with estimated posterior means and estimated posterior standard deviations and the arguments used to fit the model.
- Programming languages:
- Computer skills:
- Bayesian Generalized Linear Regression
- Restrictions to use:
- Operating system:
- Unix/Linux, Mac OS, Windows
- GNU General Public License version 3.0
- Source code URL:
- Paulino Perez Rodriguez <>
- Alessio Cecchinato <>
No open topic.
(Ferragina et al., 2015)
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
J Dairy Sci.
PMID: 26387015 DOI: 10.3168/jds.2014-9143
(Pérez and de los Campos, 2014)
Genome-wide regression and prediction with the BGLR statistical package.
PMID: 25009151 DOI: 10.1534/genetics.114.164442
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Legnaro, Italy; Epidemiology and Biostatistics Department, Michigan State University, East Lansing, MI, USA; Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
Supported by the Province of Trento (Italy); by National Institutes of Health grant 7-R01-DK-062148-10-S1; by National Institute of Health grants R01GM09992 and R01GM101219 and by National Science Foundation grant IOS-1444543, sub-award UFDSP00010707.
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