Title:   Functions and Datasets for the book "Bayesian Data Analysis"
Description: Functions for Bayesian Data Analysis, with datasets
from the book "Bayesian data Analysis (second edition)" by
Gelman, Carlin, Stern and Rubin. Not all datasets yet, hopefully
License: GPL version 2 or any later version
Packaged: Thu Jan 29 21:16:47 2009; kjetil

Title: Functions and Datasets for the book "Bayesian Data Analysis"
Description: Functions for Bayesian Data Analysis, with datasets from
the book "Bayesian data Analysis (second edition)" by Gelman,
Carlin, Stern and Rubin. Not all datasets yet, hopefully

Title: Bayesian Single-Arm Design with Survival Endpoints
Description: The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint.

Title: Bayesian Models for Dissolution Testing
Description: Fits Bayesian models to dissolution data sets that can be used for dissolution testing. Currently the package includes the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>, but more models may be added over time.

Title: Determining Effective Sample Size
Author: Jaejoon Song, Satoshi Morita, J. Jack Lee
Description: Determines effective sample size of a parametric prior distribution
in Bayesian models. To learn more about Bayesian effective sample size, see: Morita, S., Thall, P. F., & Muller, P. (2008) <https://www.jstor.org/stable/25502095>.
Imports: Rcpp, dfcrm, MatrixModels, MASS

in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.

Title: Bayesian Inference for Factor Modeling
Authors@R: person("Rémi", "Piatek", email = "remi.piatek@econ.ku.dk",
Description: Collection of procedures to perform Bayesian analysis on a variety
of factor models. Currently, it includes: Bayesian Exploratory Factor
Analysis (befa), an approach to dedicated factor analysis with stochastic
search on the structure of the factor loading matrix. The number of latent
factors, as well as the allocation of the manifest variables to the factors,
are not fixed a priori but determined during MCMC sampling.
More approaches will be included in future releases of this package.