Nicole Kraemer <nkraemer@cs.tu-berlin.de>
Anne-Laure Boulesteix <boulesteix@slcmsr.org>

Nicole Kr\"amer <nkraemer@cs.tu-berlin.de>
Anne-Laure Boulesteix <boulesteix@slcmsr.org>

Nicole Kraemer <nkraemer@cs.tu-berlin.de>
Anne-Laure Boulesteix <anne-laure.boulesteix@stat.uni-muenchen.de>

Nicole Kraemer <nkraemer@cs.tu-berlin.de> Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>
This package contains linear and nonlinear regression
methods based on Partial Least Squares and Penalization
Techniques.

Nicole Kraemer <kraemer@ma.tum.de> Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>
This package contains linear and nonlinear regression
methods based on Partial Least Squares and Penalization
Techniques. Model parameters are selected via cross-validation,
and confidence intervals ans tests for the regression
coefficients can be conducted via jackknifing.

Nicole Kraemer <nicole.kraemer@tum.de> Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>

Nicole Kraemer <kraemer_r_packages@yahoo.de> Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>
Contains linear and nonlinear regression
methods based on Partial Least Squares and Penalization
Techniques. Model parameters are selected via cross-validation,
and confidence intervals ans tests for the regression
coefficients can be conducted via jackknifing.

Privacy-Protecting Hazard Ratio Estimation in Distributed Data
Networks
Di Shu <shudi1991@gmail.com>, Sengwee Toh <darren_toh@harvardpilgrim.org>
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.

A Collection of Models that Employ a Prior Distribution on
Partitions
c(
person(given = "Garritt L.", family = "Page", email = "page@stat.byu.edu", role = c("aut", "cre", "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.  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
also provided.