BGLR: Bayesian Generalized Linear Regression. The BGLR (‘Bayesian Generalized Linear Regression’) function fits various types of parametric and semi-parametric Bayesian regressions to continuos (censored or not), binary and ordinal outcomes.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Foroutaifar, Saheb: Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data (2020)
- Gianola, Daniel; Fernando, Rohan L.; Schön, Chris-Carolin: Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression (2020)
- Kim, Youngseok; Gao, Chao: Bayesian model selection with graph structured sparsity (2020)
- Martini, Johannes W. R.; Toledo, Fernando H.; Crossa, José: On the approximation of interaction effect models by Hadamard powers of the additive genomic relationship (2020)
- Zhang, Liyuan; Khare, Kshitij; Xing, Zeren: Trace class Markov chains for the normal-gamma Bayesian shrinkage model (2019)
- Crawford, Lorin; Wood, Kris C.; Zhou, Xiang; Mukherjee, Sayan: Bayesian approximate kernel regression with variable selection (2018)
- Pal, Subahdip; Khare, Kshitij; Hobert, James P.: Trace class Markov chains for Bayesian inference with generalized double Pareto shrinkage priors (2017)
- de los Campos, Gustavo; Veturi, Yogasudha; Vazquez, Ana I.; Lehermeier, Christina; Pérez-Rodríguez, Paulino: Incorporating genetic heterogeneity in whole-genome regressions using interactions (2015)
- Pérez-Elizalde, Sergio; Cuevas, Jaime; Pérez-Rodríguez, Paulino; Crossa, José: Selection of the bandwidth parameter in a Bayesian kernel regression model for genomic-enabled prediction (2015)
- Tempelman, Robert J.: Statistical and computational challenges in whole genome prediction and genome-wide association analyses for plant and animal breeding (2015)