Browse Items (20 total)

This article introduces the R package BayesCTDesign for two-arm randomized Bayesian
trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The…

Missing data occur in many types of studies and typically complicate the analysis.
Multiple imputation, either using joint modeling or the more flexible fully conditional
specification approach, are popular and work well in standard settings. In…

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for…

There have been considerable methodological developments of Bayes factors for hypothesis testing in the social and behavioral sciences, and related fields. This development
is due to the flexibility of the Bayes factor for testing multiple…

Booming in business and a staple analysis in medical trials, the A/B test assesses
the effect of an intervention or treatment by comparing its success rate with that of a
control condition. Across many practical applications, it is desirable that…

We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we
consider the case when S matrices are available, each describing the…

BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust
generalization of the popular class of Dirichlet process mixture models. A variety…

Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed
to deal with their dense parameterization, imposing structure on model…

Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting
in TVP models is well known. This issue can be dealt with using…

Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the
large number of latent quantities, their efficient estimation is…