PReMiuM

R package PReMiuM: 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.


References in zbMATH (referenced in 13 articles , 2 standard articles )

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  1. Abolhassani, Ali; Prates, Marcos O.: An up-to-date review of scan statistics (2021)
  2. Corradin, R., Canale, A.,Nipoti, B: BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures (2021) not zbMATH
  3. Liverani, Silvia; Leigh, Lucy; Hudson, Irene L.; Byles, Julie E.: Clustering method for censored and collinear survival data (2021)
  4. Nguyen K. Huynh, Sergio Bejar, Vineeta Yadav, Bumba Mukherjee: IDCeMPy: Python Package for Inflated Discrete Choice Models (2021) not zbMATH
  5. Crook, Oliver M.; Gatto, Laurent; Kirk, Paul D. W.: Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics (2019)
  6. Virgilio Gómez-Rubio; Paula Moraga; John Molitor; Barry Rowlingson: DClusterm: Model-Based Detection of Disease Clusters (2019) not zbMATH
  7. Wang, Ketong; Porter, Michael D.: Optimal Bayesian clustering using non-negative matrix factorization (2018)
  8. Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia; Grün, Bettina: Model-based clustering based on sparse finite Gaussian mixtures (2016)
  9. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016) not zbMATH
  10. Papathomas, Michail; Richardson, Sylvia: Exploring dependence between categorical variables: benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms (2016)
  11. Argiento, Raffaele; Guglielmi, Alessandra; Hsiao, Chuhsing Kate; Ruggeri, Fabrizio; Wang, Charlotte: Modeling the association between clusters of SNPs and disease responses (2015)
  12. Hastie, David I.; Liverani, Silvia; Richardson, Sylvia: Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations (2015)
  13. Silvia Liverani, David I. Hastie, Lamiae Azizi, Michail Papathomas, Sylvia Richardson: PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes (2013) arXiv