for FREE

BFRM was specially created to be a comprehensive implementation of sparse statistical models. It was built for high-dimensional data analysis, structure discovery and prediction.

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.