https://jennychang32.r-universe.devRecent package updates in jennychang32R-universehttps://github.com/jennychang32.pnghttps://jennychang32.r-universe.devTue, 03 Nov 2026 15:30:02 GMTchang103@msu.edu (Chun-Yi Chang)Fits two-layer deep intrinsic coregionalization Gaussian process surrogate models for multiple outputs observed at shared input locations. Uses Markov chain Monte Carlo sampling for kernel parameters and latent coordinates, with plug-in coregionalization estimates. Provides burn-in removal and thinning, posterior predictive means and within-location output covariances, and sequential experimental design using an active learning criterion. The method is described by Chang and Sung (2026) <doi:10.1615/Int.J.UncertaintyQuantification.2026062570>. Adapts code from the 'deepgp' package, with underlying deep Gaussian process methodology described by Sauer, Gramacy, and Higdon (2023) <doi:10.1080/00401706.2021.2008505>.https://github.com/r-universe/jennychang32/actions/runs/41836302054Tue, 03 Nov 2026 15:30:02 GMTdeepICMGP0.1.1successhttps://jennychang32.r-universe.devhttps://github.com/cran/deepICMGP