The R Package BHAM: Fast and Scalable Bayesian Hierarchical Additive Model for High-dimensional Data

2022/07/05 by Boyi Guo, Guo, Boyi, Nengjun Yi +1
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Computation (stat.CO) #FOS: Computer and information sciences #Gene expression and cancer classification #Protein Structure and Dynamics

paper · pdf · doi:10.48550/arxiv.2207.02348

openalex publication_date 2022/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

BHAM is a freely avaible R pakcage that implments Bayesian hierarchical additive models for high-dimensional clinical and genomic data. The package includes functions that generalized additive model, and Cox additive model with the spike-and-slab LASSO prior. These functions implement scalable and stable algorithms to estimate parameters. BHAM also provides utility functions to construct additive models in high dimensional settings, select optimal models, summarize bi-level variable selection results, and visualize nonlinear effects. The package can facilitate flexible modeling of large-scale molecular data, i.e. detecting susceptible variables and infering disease diagnostic and prognostic. In this article, we describe the models, algorithms and related features implemented in BHAM. The package is freely available via the public GitHub repository https://github.com/boyiguo1/BHAM.

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