https://ghoshstats.r-universe.devRecent package updates in ghoshstatsR-universehttps://github.com/ghoshstats.pnghttps://ghoshstats.r-universe.devTue, 19 May 2026 08:42:26 GMTsameer.deshpande@wisc.edu (Sameer K. Deshpande)Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) <doi:10.48550/arXiv.2510.08204>.https://github.com/r-universe/cran/actions/runs/26093929736Tue, 19 May 2026 08:42:26 GMTsparseVCBART1.0.0successhttps://cran.r-universe.devhttps://github.com/cran/sparseVCBARTsameer.deshpande@wisc.edu (Sameer K. Deshpande)Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) <doi:10.48550/arXiv.2510.08204>.https://github.com/r-universe/ghoshstats/actions/runs/26150486099Wed, 13 May 2026 14:36:01 GMTsparseVCBART1.0.0successhttps://ghoshstats.r-universe.devhttps://github.com/ghoshstats/sparsevcbart