Package: PReMiuM
Type: Package
Title: Dirichlet Process Bayesian Clustering, Profile Regression
Version: 3.2.14
Authors@R: c(
    person(given = c("David", "I."),
           family = "Hastie",
           role = "aut"),
    person(given = "Silvia",
           family = "Liverani",
           role = c("aut", "cre"),
           email = "liveranis@gmail.com"),
    person(given = "Sylvia",
           family = "Richardson",
           role = "aut"),
    person(given = c("Aurore", "J."),
           family = "Lavigne",
           role = "aut"),
    person(given = "Lucy",
           family = "Leigh",
           role = "aut"),
    person(given = "Lamiae",
           family = "Azizi",
           role = "aut"),
    person(given = "Xi",
           family = "Liu",
           role = "aut"),
    person(given = "Ruizhu",
           family = "Huang",
           role = "aut"),
    person(given = "Austin",
           family = "Gratton",
           role = "aut"),
    person(given = "Wei",
           family = "Jing",
           role = "aut")
 )
Description: 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. The main reference for the package is Liverani,
        Hastie, Azizi, Papathomas and Richardson (2015)
        <doi:10.18637/jss.v064.i07>.
URL: https://www.silvialiverani.com/software/
License: GPL-2
LazyLoad: yes
Depends: R (>= 3.5.1)
Imports: Rcpp (>= 0.12.13), ggplot2 (>= 2.2), cluster, plotrix (>=
        3.6-6), data.table (>= 1.10.4-3), spdep (>= 0.7-7), sf (>=
        1.0-8)
Suggests: testthat (>= 1.0.2)
LinkingTo: Rcpp, RcppEigen (>= 0.3.3.3.0), BH (>= 1.65.0-1)
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2026-09-24 16:40:55 UTC; root
Author: David I. Hastie [aut], Silvia Liverani [aut, cre], Sylvia
        Richardson [aut], Aurore J. Lavigne [aut], Lucy Leigh [aut],
        Lamiae Azizi [aut], Xi Liu [aut], Ruizhu Huang [aut], Austin
        Gratton [aut], Wei Jing [aut]
Maintainer: Silvia Liverani <liveranis@gmail.com>
Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev
        make libssl-dev libproj-dev libsqlite3-dev libudunits2-dev
Repository: https://silvialiverani.r-universe.dev
Date/Publication: 2026-09-24 15:16:13 UTC
RemoteUrl: https://github.com/cran/PReMiuM
RemoteRef: HEAD
RemoteSha: c0ac6fae2a70d933183aaa3d7589adaf16184e22