DDPstar: Density Regression via Dirichlet Process Mixtures of Normal
Structured Additive Regression Models
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.
| Version: |
1.0-1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, grDevices, graphics, splines, moments, Matrix, parallel, MASS |
| Published: |
2025-01-31 |
| DOI: |
10.32614/CRAN.package.DDPstar |
| Author: |
Maria Xose Rodriguez-Alvarez
 [aut, cre],
Vanda Inacio
[aut] |
| Maintainer: |
Maria Xose Rodriguez-Alvarez <mxrodriguez at uvigo.gal> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: |
no |
| CRAN checks: |
DDPstar results |
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