Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

We provide user friendly software for Bayesian analysis of functional data models using \pkg{WinBUGS}~1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.

The absence of user-friendly software has long been a major obstacle to the routine application of Bayesian methods in business and industry. It will only be through widespread application of the Bayesian approach to real problems that issues, such as the use of prior distributions, can be practically resolved in the same way that the choice of significance levels has been in the classical approach; although most Bayesians would hope for a much more satisfactory resolution. It is only relatively recently that any general purpose Bayesian software has been available; by far the most widely used such package isWinBUGS. Although this software has been designed to enable an extremely wide variety of models to be coded relatively easily, it is unlikely that many will bother to learn the language and its nuances unless they are already highly motivated to try Bayesian methods. This paper describes a graphical user interface, programmed by the author, which facilitates the specification of a wide class of generalised linear mixed models for analysis using WinBUGS. The program, BugsXLA (v2.1), is an Excel Add-In that not only allows the user to specify a model as one would in a package such as SAS or S-PLUS, but also aids the specification of priors and control of the MCMC run itself. Inevitably, developing a program such as this forces one to think again about such issues as choice of default priors, parameterisation and assessing convergence. I have tried to adopt currently perceived good practices, but mainly share my approach so that others can apply it and, through constructive criticism, play a small part in the ultimate development of the first Bayesian software package truly useable by the average data analyst.

Conditional autoregressive models are commonly used to represent spatial autocorrelation in data relating to a set of non-overlapping areal units, which arise in a wide variety of applications including agriculture, education, epidemiology and image analysis. Such models are typically specified in a hierarchical Bayesian framework, with inference based on Markov chain Monte Carlo (MCMC) simulation. The most widely used software to fit such models is WinBUGS or OpenBUGS, but in this paper we introduce the R package CARBayes. The main advantage of CARBayes compared with the BUGS software is its ease of use, because: (1) the spatial adjacency information is easy to specify as a binary neighbourhood matrix; and (2) given the neighbourhood matrix the models can be implemented by a single function call in R. This paper outlines the general class of Bayesian hierarchical models that can be implemented in the CARBayes software, describes their implementation via MCMC simulation techniques, and illustrates their use with two worked examples in the fields of house price analysis and disease mapping.

Missing data is a common problem in survey based research. There are many packages that compensate for missing data but few can easily compensate for missing longitudinal data. WinBUGScompensates for missing data using multiple imputation, and is able to incorporate longitudinal structure using random effects. We demonstrate the superiority of longitudinal imputation over cross-sectional imputation using WinBUGS. We use example data from the Australian Longitudinal Study on Women's Health. We give a SAS macro that uses WinBUGS to analyze longitudinal models with missing covariate date, and demonstrate its use in a longitudinal study of terminal cancer patients and their carers.

A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an independent-voxels treatment inefficient in its use of the data. Bayesian random effect models are expected to be more efficient owing to their information-borrowing behaviour.
To illustrate the Bayesian random effects approach, this paper outlines a Markov chain Monte Carlo (MCMC) analysis of a perfusion MRI dataset, implemented in R using the BRugs package. BRugs provides an interface to WinBUGS and its GeoBUGS add-on. WinBUGS is a widely used programme for performing MCMC analyses, with a focus on Bayesian random effect models. A simultaneous modeling of both voxels (restricted to a region of interest) and multiple subjects is demonstrated. Despite the low signal-to-noise ratio in the magnetic resonance signal intensity data, useful model signal intensity profiles are obtained. The merits of random effects modeling are discussed in comparison with the alternative approaches based on region-of-interest averaging and repeated independent voxels analysis.
This paper focuses on perfusion MRI for the purpose of illustration, the main proposition being that random effects modeling is expected to be beneficial in many other MRI applications in which the signal-to-noise ratio is a limiting factor.

The R2WinBUGS package provides convenient functions to call WinBUGS from R. It automatically writes the data and scripts in a format readable by WinBUGS for processing in batch mode, which is possible since version 1.4. After the WinBUGS process has finished, it is possible either to read the resulting data into R by the package itself--which gives a compact graphical summary of inference and convergence diagnostics--or to use the facilities of the coda package for further analyses of the output. Examples are given to demonstrate the usage of this package.

This is a macro which facilitates remote execution of WinBUGS from within SAS. The macro pre-processes data for WinBUGS, writes the WinBUGS batch-script, executes this script and reads in output statistics from the WinBUGS log-file back into SAS native format. The user specifies the input and output file names and directory path as well as the statistics to be monitored in WinBUGS. The code works best for a model that has already been set up and checked for convergence diagnostics within WinBUGS. An obvious extension of the use of this macro is for running simulations where the input and output files all have the same name but all that differs between simulation iterations is the input dataset. The functionality and syntax of the macro call are described in this paper and illustrated using a simple linear regression model.