Construction of Regular and Irregular Histograms with Different
Options for Automatic Choice of Bins
c(person("Thoralf", "Mildenberger", role = c("aut", "cre"),
email = "mild@zhaw.ch"),
person("Yves", "Rozenholc", role = "aut"),
person("David", "Zasada", role = "aut"))
Thoralf Mildenberger [aut, cre],
Yves Rozenholc [aut],
David Zasada [aut]
Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).

Non-Longitudinal Bayesian Historical Borrowing Models
Historical borrowing in clinical trials can improve
precision and operating characteristics. This package supports
a hierarchical model and a mixture model to borrow historical
control data from other studies to better characterize the
control response of the current study. It also quantifies
the amount of borrowing through benchmark models (independent
and pooled). Some of the methods are discussed by
Viele et al. (2013) <doi:10.1002/pst.1589>.
c(
person(
given = c("William", "Michael"),
family = "Landau",
role = c("aut", "cre"),
email = "will.landau@gmail.com",
comment = c(ORCID = "0000-0003-1878-3253")
),
person(
given = "Albert",
family = "Man",
role = "rev",
email = "man_albert@lilly.com"
),
person(
family = "Eli Lilly and Company",
role = "cph"
))
dplyr, ggplot2, Matrix, posterior, rjags, rlang, stats,
tibble, tidyr, tidyselect, utils
William Michael Landau [aut, cre]
(<https://orcid.org/0000-0003-1878-3253>),
Albert Man [rev],
Eli Lilly and Company [cph]

Longitudinal Bayesian Historical Borrowing Models
Historical borrowing in clinical trials can improve
precision and operating characteristics. This package supports
a longitudinal hierarchical model to borrow historical
control data from other studies to better characterize the
control response of the current study. It also quantifies
the amount of borrowing through longitudinal benchmark models (independent
and pooled). The hierarchical model approach to historical borrowing
is discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.
clustermq, dplyr, ggplot2, MASS, Matrix, methods, posterior,
Rcpp, RcppParallel, rlang, rstan, rstantools, stats, tibble,
tidyr, tidyselect, trialr, utils, withr, zoo
BH, Rcpp, RcppEigen, RcppParallel, rstan, StanHeaders