The framework of sparse latent factor modelling coupled with sparse regression and anova for multivariate data is relevant in many exploratory and predictive problems with very high-dimensional multivariate observations. Bayesian analysis utilising sparsity-inducing models, and computational methods able to efficiently explore and fit large-scale models, now allow these approaches to be used in increasingly complex and high-dimensional problems.

The statistical methods and computational analysis represented in BFRM are generic and will apply in many areas of application. Some recent applications include studies in finance and econometrics and other areas. A major focus for applications is in biological studies using gene expression data coupled with outcomes (phenotypes) to be predicted based on patterns underlying gene expression, and especially for biological pathway analysis and the evaluation of subpathway structure. Examples of the use of the program in this area will be provided shortly.

• Carvalho et al, 2008, High-dimensional sparse factor modelling: Applications in gene expression genomics, in JASA
• Lucas et al 2006, Sparse statistical modelling in gene expression genomics, in Bayesian Bioinformatics
• Seo et al 2008, Of mice and men: Sparse statistical modelling in cardiovascular genomics, in Annals of Applied Statistics
• Lucas et al 2009, Bench-to-bedside and cross-study projections of genomic biomarkers: An evaluation in breast cancer genomics, in PLoS One
• Lucas et al 2009, A Bayesian analysis strategy for cross-study translation of gene expression biomarkers, in Statistical Applications in Genetics and Molecular Biology
• Merl et al 2009, Trans-study projection of genomic biomarkers in analysis of oncogene deregulation and breast cancer, in The Handbook of Applied Bayesian Analysis
• Useful additional discussion and examples appear in the 2006 PhD theses of Carlos Carvalho and Joe Lucas.

The research and development underlying BFRM was supported, in part, by the National Science Foundation (grants DMS-0102227 and 0342172) and the National Institutes of Health (grants HL-73042 and CA-112952). Any opinions, findings and conclusions or recomendations expressed in this work are those of the authors and do not necessarily reflect the views of the NSF or NIH.

It is understood by the user that neither the authors nor Duke University bear any responsibility nor assume any liability for any end-use of this software. It is expected that appropriate credit/acknowledgement be given should the software be included as an element in other software development or in publications.

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