{ "_id": "69d3673610581d1350d91133", "Package": "vmsae", "Title": "Variational Multivariate Spatial Small Area Estimation", "Version": "0.1.2", "Maintainer": "Zhenhua Wang ", "Authors@R": "c(\nperson(\"Zhenhua\", \"Wang\", email = \"zhenhua.wang@missouri.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Paul A.\", \"Parker\", email = \"pparker1@ucsc.edu\", role = c(\"aut\", \"res\")),\nperson(\"Scott H.\", \"Holan\", email = \"holans@missouri.edu\", role = c(\"aut\", \"res\")))", "Description": "Variational Autoencoded Multivariate Spatial Fay-Herriot\nmodels are designed to efficiently estimate population\nparameters in small area estimation. This package implements\nthe variational generalized multivariate spatial Fay-Herriot\nmodel (VGMSFH) using 'NumPyro' and 'PyTorch' backends, as\ndemonstrated by Wang, Parker, and Holan (2025)\n. The 'vmsae' package provides\nutility functions to load weights of the pretrained variational\nautoencoders (VAEs) as well as tools to train custom VAEs\ntailored to users specific applications.", "URL": "https://github.com/zhenhua-wang/vmsae", "BugReports": "https://github.com/zhenhua-wang/vmsae/issues", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibicu-dev libpng-dev libssl-dev libproj-dev python3\nlibsqlite3-dev libudunits2-dev", "Repository": "https://zhenhua-wang.r-universe.dev", "Date/Publication": "2025-10-08 22:54:10 UTC", "RemoteUrl": "https://github.com/zhenhua-wang/vmsae", "RemoteRef": "HEAD", "RemoteSha": "dbc598dfce06657b40fdf0bd448ec660bb082bbb", "NeedsCompilation": "no", "Packaged": { "Date": "2026-04-06 07:52:43 UTC", "User": "root" }, "Author": "Zhenhua Wang [aut, cre],\nPaul A. Parker [aut, res],\nScott H. 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