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…

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…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model
estimation is complicated by the fact that we typically have multiple…

The INLA package provides a tool for computationally efficient Bayesian modeling
and inference for various widely used models, more formally the class of latent Gaussian
models. It is a non-sampling based framework which provides approximate…

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…

Generalized additive models (GAMs) are flexible non-linear regression models, which
can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R
package. While the GAM methods provided by mgcv are based on the assumption…

In this summary we introduce the papers published in the special issue on Bayesian
statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian
inference on different topics such as general packages for hierarchical linear…

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model
specification and the ability to program model-generic algorithms. Specifically,…

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…

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…