Package: spikeslab Version: 1.1.6 BUILD: bld20220426 --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.1.6 o CRAN compliance update. --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.1.4 o This build corrects a long-standing numerical issue with the BMA that can occur under certain atypical settings" --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.1.3 o improved interconnectivity between user functions. Enhancements to cv.spikeslab. o compression of data files. --------------------------------------------------------------------------------- CHANGES TO 1.1.2 o minor fixes to R-side wrapper, adjustments to packaging, and documentation. --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.1.0 RELEASE 1.1.0 is a recommended upgrade of the product. o cv.spikeslab() now takes advantage of the CRAN package "snow". It allows users to create a socket cluser on the local machine, enabling parallel execution of this function. It scales with the number of CPU cores on the local machine. --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.0.3 o minor fixes to R-side wrapper, adjustments to packaging, and documentation. --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.0.2 o minor adjustments to packaging --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.0.1 o minor adjustments to the R-side wrappers and documentation --------------------------------------------------------------------------------- CHANGES TO RELEASE 1.0.0 RELEASE 1.0.0 is the first and initial release of this package. Fits a rescaled spike and slab model using a continuous bimodal prior. Can be used for prediction and variable selection in low and high-dimensional linear regression models. Key features include: o Option for ultra-fast handling of high-dimensional data. o Variable selection using the generalized elastic net (gnet). o Grouping of variables with unique regularization (no limit on the number). o Predict wrapper for predicting on test data. o Sparse PC approach for multiclass analysis of gene expression data. o Depends on the randomForest and lars R-packages. ---------------------------------------------------------------------------------