BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology

Bioinformatics
Nengjun YiBoyi Guo

Abstract

BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM. The package is freely available from the public GitHub repository, https://github.com/nyiuab/BhGLM.

Citations

Nov 30, 2012·Statistical Applications in Genetics and Molecular Biology·Nengjun Yi, Shuangge Ma
Jan 31, 2015·The New England Journal of Medicine·Francis S Collins, Harold Varmus
Mar 1, 2011·Journal of Statistical Software·Noah SimonRob Tibshirani
Jun 29, 2018·Bioinformatics·Wensheng ZhangKun Zhang

Related Concepts

2-Dimensional
Genome
Genomics
Student
Epidemiology
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