Author: Nicole Kraemer <kraemer_r_packages@yahoo.de> Anne-Laure Boulesteix
Description: Contains linear and nonlinear regression

Title: Privacy-Protecting Hazard Ratio Estimation in Distributed Data
Author: Di Shu <shudi1991@gmail.com>, Sengwee Toh <darren_toh@harvardpilgrim.org>
Description: An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.

Title: A Collection of Models that Employ a Prior Distribution on
person(given = "Garritt L.", family = "Page", email = "page@stat.byu.edu", role = c("aut", "cre", "cph")),
Description: 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.  In addition, 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> is

<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)

Title: A Collection of Models that Employ a Product Parition Prior
person(given = "Jose", family = "J. Quinlan", email = "jjquinla@uc.cl", role = c("aut", "cph")),

Title: A Collection of Models that Employ a Product Partition
Description: 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.;