Browse Items (20 total)

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…

ABCpy is a highly modular scientific library for approximate Bayesian computation
(ABC) written in Python. The main contribution of this paper is to document a software
engineering effort that enables domain scientists to easily apply ABC to their…

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…

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…

Item response theory (IRT) is widely applied in the human sciences to model persons’
responses on a set of items measuring one or more latent constructs. While several
R packages have been developed that implement IRT models, they tend to be…

In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with
high-dimensional genomic and other omics data, a problem that can be studied…

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…

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…

Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many
biological studies seek to understand non-deterministic patterns in the…