This package provides an Rcpp reimplementation of the Bayesian non-parametric Dirichlet Process Regression model first published in Zeng & Zhou 2017 (https://doi.org/10.1038/s41467-017-00470-2). A full Bayesian version is implemented with Gibbs sampling, as a well a faster but less accurate variational Bayes approximation.
Dirichlet Process Regression is a generalization of penalized regression methods such as ridge regression, LASSO, and elastic net . These methods can be understood from a Bayesian perspective as setting a prior on the effect sizes of the coefficients. Ridge regression, for example, is equivalent to a Gaussian prior, while LASSO is equivalent to a Laplace prior. Ultimately these prior can all be parameterized as specific scale mixtures of normal distributions. DPR generalizes this model by allowing the prior to be a scale mixture of an arbitrary number of normal distributions. The Adaptive Gibbs sampling mode provides one strategy for choosing this number.
To install the development version of the package, use remote::install_github:
remotes::install_github("mohammed321/RcppDPR")