![]() ![]() ![]() Alternatively, for convenience, the pre-compiled MEX files (MATLAB R2017a) for Windows, Linux and Mac OSX can be downloaded from the following URL: To compile the C++ code, run compile.m from the bayesreg directory within MATLAB compilation requires the MS Visual Studio Professional or the GNU g++ compiler. ![]() The package now handles logistic regression without the need for MEX files, but big speed-ups can be obtained when using compiled code, so this is recommended. Fix count regression for Matlab 2020a and 2020b releases. High-Dimensional Bayesian Regularised Regression with the BayesReg Package To install the R package, type "install.packages("bayesreg")" within R. An R version of this toolbox is now available on CRAN. Please see the scripts in the directory "examples\" for examples on how to use the toolbox, or type "help bayesreg" within MATLAB. ![]() The toolbox is very efficient and can be used with high-dimensional data. Most features are straightforward to use and the toolbox can work directly with MATLAB tables (including automatically handling categorical variables), or you can use standard MATLAB matrices. To support analysis of data with outliers, we provide two heavy-tailed error models in our implementation of Bayesian linear regression: Laplace and Student-t distribution errors. This can be used to exploit a priori knowledge regarding predictors and how they may be related to each other (for example, in grouping genetic data into genes and collections of genes such as pathways).Ĭount regression is now supported through implementation of Poisson and geometric regression models. The toolbox allows predictors to be assigned to logical groupings (potentially overlapping, so that predictors can be part of multiple groups). The lasso, horseshoe, horseshoe+ and log-t priors are recommended for data sets where the number of predictors is greater than the sample size, and the log-t prior provides adaptation to unknown levels of sparsity. The toolbox provides highly efficient and numerically stable implementations of ridge, lasso, horseshoe, horseshoe+, log-t and g-prior regression. This is a comprehensive, user-friendly toolbox implementing the state-of-the-art in Bayesian linear regression, logistic and count regression. ![]()
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