The BHAM package provides a scalable solution for fitting high-dimensional generalized additive model using spike-and-slab lasso priors or other regularized priors, including continuous spike-and-slab priors, Student’ T priors and double exponential priors. It fits linear, logistic, poisson and Cox regression models. The specification of the additive functions follows a popular syntax implemented in mgcv. An arsenal of facilitating functions are provided, including cross-validation, model summary, and visualization.

## Getting Started

If you are new to BHAM we recommend starting with the vignettes

## Installation

Install the latest development version from GitHub

if (!require(devtools)) {
install.packages("devtools")
}
devtools::install_github("boyiguo1/BHAM", build_vignettes = FALSE)

You can also set build_vignettes=TRUE but this will slow down the installation drastically (the vignettes can always be accessed online anytime at boyiguo1.github.io/BHAM/articles).

We are currently streamlining the syntax of the package for better user experience. When we have a stable version, we will submit the package to CRAN. Please stay tuned!