BGLR specifications

Information


Unique identifier OMICS_21950
Name BGLR
Alternative name Bayesian Generalized Linear Regression
Software type Package/Module
Restrictions to use None
Output data A list with estimated posterior means and estimated posterior standard deviations and the arguments used to fit the model.
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.0.7
Stability Stable
Requirements
R(≥2.10)
Maintained Yes

Download


Versioning


Add your version

Maintainers


  • person_outline Paulino Perez Rodriguez <>
  • person_outline Alessio Cecchinato <>

Publications for Bayesian Generalized Linear Regression

BGLR in publications

 (29)
PMCID: 5940167
PMID: 29588381
DOI: 10.1534/g3.117.300406

[…] of the parameters for implementing a gibbs sampler can be found elsewhere (e.g., ). we set the hyper-parameters using the same rules as in the bsn model. the brr model can be fitted easily using the bglr statistical package ()., in this section, we use simulated data using marker genotypes from a wheat dataset made publicly available by . the dataset includes genotypic information for 599 wheat […]

PMCID: 5852557
PMID: 29370075
DOI: 10.3390/genes9020061

[…] and tools exist in the literature to measure heritability among a set of unrelated individuals. these include mixed model approaches (gcta, reacta, plink, etc.) [,], bayesian approaches (example bglr) [], ld based weighted methods in mixed linear model approaches (ldak) [], and machine learning approaches (herra and megha) [,]. all of these methods are focused towards explaining the additive […]

PMCID: 5784162
PMID: 29367617
DOI: 10.1038/s41598-018-19696-1

[…] bayesa, and bayesb. the bayesian models we tested allow for alternative genetic architecture by way of differential shrinkage of marker effects. we performed bayesian predictions with the r package bglr., random forest (rf) is a machine learning method used for regression and classification–. random forest regression with marker data has been shown to capture epistatic effects […]

PMCID: 5919744
PMID: 29255118
DOI: 10.1534/g3.117.300199

[…] a normal distribution e∼n(0,iσ2), where i is the identity matrix. the gblup and rrblup implementations of the synbreed r package () were used., additionally, a bayesb () model as implemented in the bglr r package () was used to estimate marker effects and to calculate prediction ability. bayesb uses the same linear model as rrblup, but a prior for the marker effects is modeled as mixture […]

PMCID: 5787227
PMID: 29138904
DOI: 10.1007/s00122-017-3011-4

[…] jacquin et al. (). for bayesb, the model that specified two component mixtures prior with a point of mass at zero and a scaled-t slab for marker effect (meuwissen et al. ) was implemented using the bglr statistical package (pérez and de los campos ). the default parameters for prior specification were used and the number of iterations for the markov chain monte carlo (mcmc) algorithm was set […]


To access a full list of publications, you will need to upgrade to our premium service.

BGLR institution(s)
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
BGLR funding source(s)
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.

BGLR reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review BGLR