Provides functions that fit hierarchical Gaussian and probit ordinal models.
A (covariate dependent) product partition model is used as a prior.
If a covariate dependent product partition model  is selected,
then all the options detailed in Page, G.L.; Quintana, F.A.; (2018)
<doi:10.1007/s11222-017-9777-z> are available.  If covariate values
are missing, then the approach detailed in Page, G.L.; Quintana, F.A.;
Mueller, P (2020) <arXiv:1912.13119> is employed.  Also included in the
package is a function that fits a Gaussian likelihood spatial product
partition model that is detailed in Page, G.L.; Quintana, F.A.; (2016)
<doi:10.1214/15-BA971>.

A Collection of Models that Employ a Product Parition Prior
Distribution on Partitions
c(
person(given = "Garritt L.", family = "Page", email = "page@stat.byu.edu", role = c("aut", "cre", "cph")),
person(given = "Jose", family = "J. Quinlan", email = "jjquinla@uc.cl", role = c("aut", "cph")),
person(given = "S. McKay", family = "Curtis", email = "s.mckay.curtis@gmail.com", role = c("ctb", "cph")),
person(given = "Radford M.", family = "Neal", role = c("ctb", "cph")))
Provides functions that fit hierarchical Gaussian and probit ordinal models.
A (covariate dependent) product partition model is used as a prior.
If a covariate dependent product partition model  is selected,
then all the options detailed in Page, G.L.; Quintana, F.A.; (2018)
<doi:10.1007/s11222-017-9777-z> are available.  If covariate values
are missing, then the approach detailed in Page, G.L.; Quintana, F.A.;
Mueller, P (2020) <arXiv:1912.13119> is employed.  Also included in the
package is a function that fits a Gaussian likelihood spatial product
partition model that is detailed in Page, G.L.; Quintana, F.A.; (2016)
<DOI:10.1214/15-BA971>.
Garritt L. Page [aut, cre, cph],
Jose J. Quinlan [aut, cph],
S. McKay Curtis [ctb, cph],
Radford M. Neal [ctb, cph]

A Collection of Models that Employ a Product Partition
Distribution as a Prior on Partitions
Provides a suite of functions that fit models that use PPM type priors for partitions.
Models include hierarchical Gaussian and probit ordinal models with  a (covariate
dependent) PPM.  If a covariate dependent product partition model is selected,
then all the options detailed in Page, G.L.; Quintana, F.A. (2018)
<doi:10.1007/s11222-017-9777-z> are available.  If covariate values are missing,
then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020)
<doi:10.1080/10618600.2021.1999824> is employed.  Also included in the package is
a function that fits a Gaussian likelihood spatial product  partition model that is
detailed in Page, G.L.; Quintana, F.A. (2016)  <doi:10.1214/15-BA971>, and
multivariate change point models that are detailed in Quinlan, J.J.; Page, G.L.;
Castro, L.M. (2021) <arXiv:2201.07830>.