DDPstar-package {DDPstar} | R Documentation |
Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models
Description
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.
Details
Index of help topics:
DDPstar Density Regression via Dirichlet Process Mixtures (DDP) of Normal Structured Additive Regression (STAR) Models DDPstar-package Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models dde Dichlorodiphenyldichloroethylene (DDE) and preterm delivery data f Defining smooth terms in DDPstar formulae mcmccontrol Markov chain Monte Carlo (MCMC) parameters predict.DDPstar Predictions from fitted DDPstar models predictive.checks.DDPstar Posterior predictive checks. print.DDPstar Print method for DDPstar objects priorcontrol Prior information for the DDPstar model quantileResiduals Quantile residuals. rae Defining random effects in DDPstar formulae summary.DDPstar Summary method for DDPstar objects
Author(s)
Maria Xose Rodriguez-Alvarez [aut, cre] (<https://orcid.org/0000-0002-1329-9238>), Vanda Inacio [aut] (<https://orcid.org/0000-0001-8084-1616>)
Maintainer: Maria Xose Rodriguez-Alvarez <mxrodriguez@uvigo.gal>
References
Eilers, P.H.C. and Marx, B.D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89-121.
Eilers, P.H.C. and Marx, B.D. (2003). Multidimensional calibration with temperature interaction using two- dimensional penalized signal regression. Chemometrics and Intelligence Laboratory Systems, 66, 159-174.
De Iorio, M., Muller, P., Rosner, G. L., and MacEachern, S. N. (2004). An anova model for dependent random measures. Journal of the American Statistical Association, 99(465), 205-215
De Iorio, M., Johnson, W. O., Muller, P., and Rosner, G. L. (2009). Bayesian nonparametric nonproportional hazards survival modeling. Biometrics, 65, 762–775.
Lang, S. and Brezger, A. (2004). Bayesian P-splines. Journal of Computational and Graphical Statistics, 13(1), 183-212.
Lee, D.-J., Durban, M., and Eilers, P. (2013). Efficient two-dimensional smoothing with P- spline ANOVA mixed models and nested bases. Computational Statistics & Data Analysis, 61, 22-37.
Rodriguez-Alvarez, M. X, Inacio, V. and Klein N. (2025). Density regression via Dirichlet process mixtures of normal structured additive regression models. Accepted for publication in Statistics and Computing (DOI: 10.1007/s11222-025-10567-0).