Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models
tools:::Rd_package_title("DDPstar")
Density Regression via Dirichlet Process Mixtures (DDP) of Normal Stru...
Defining smooth terms in DDPstar formulae
Markov chain Monte Carlo (MCMC) parameters
Predictions from fitted DDPstar models
Posterior predictive checks.
Print method for DDPstar objects
Prior information for the DDPstar model
Quantile residuals.
Defining random effects in DDPstar formulae
Summary method for DDPstar objects
Implements a flexible, versatile, and computationally tractable model for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. The model assumes an additive structure for the mean of each mixture component and the effects of continuous covariates are captured through smooth nonlinear functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. The proposed method can also easily deal with parametric effects of categorical covariates, linear effects of continuous covariates, interactions between categorical and/or continuous covariates, varying coefficient terms, and random effects. Please see Rodriguez-Alvarez, Inacio et al. (2025) for more details.