DDPstar
1.0-1Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models
Overview
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.
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Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 58%
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Code & Tests
Datasets
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0-1Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-01-31
- Total releases
- 1 / 1 yrs
- License
- GPL
- Minimum R
- ≥ 3.5.0
- Bundled data
- 16 KB / 1 file
- Download size
- 54 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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