DDPstar

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.

Last 30 days

This package has been downloaded 502 times in the last 30 days. More downloads than an obscure whitepaper, but not enough to bring down any servers. A solid effort! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 28 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 759 times in the last 365 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The day with the most downloads was Mar 04, 2025 with 49 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.