Dirichlet Process Bayesian Clustering, Profile Regression

Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("PReMiuM")

3.1.6 by Silvia Liverani, 22 days ago


http://www.silvialiverani.com/software/


Browse source code at https://github.com/cran/PReMiuM


Authors: David I. Hastie, Silvia Liverani <liveranis@gmail.com> and Sylvia Richardson with contributions from Aurore J. Lavigne, Lucy Leigh, Lamiae Azizi, Xi Liu, Ruizhu Huang


Documentation:   PDF Manual  


Task views: Bayesian Inference, Cluster Analysis & Finite Mixture Models, Analysis of Spatial Data, Survival Analysis


GPL-2 license


Imports Rcpp, ggplot2, cluster, plotrix, gamlss.dist, ald

Suggests testthat

Linking to Rcpp, RcppEigen, BH

System requirements: GNU make


See at CRAN