{ "_id": "6ad4960d712795fb4dcb0838", "Package": "VeccTMVN", "Type": "Package", "Title": "Multivariate Normal Probabilities using Vecchia Approximation", "Version": "1.3.2", "Date": "2026-01-26", "Authors@R": "c(person(\"Jian Cao\", role = c(\"aut\", \"cre\"), email = \"jcao2416@gmail.com\"),\nperson(\"Matthias Katzfuss\", role = \"aut\"))", "Author": "Jian Cao [aut, cre], Matthias Katzfuss [aut]", "Maintainer": "Jian Cao ", "Description": "Under a different representation of the multivariate\nnormal (MVN) probability, we can use the Vecchia approximation\nto sample the integrand at a linear complexity with respect to\nn. Additionally, both the SOV algorithm from Genz (92) and the\nexponential-tilting method from Botev (2017) can be adapted to\nlinear complexity. The reference for the method implemented in\nthis package is Jian Cao and Matthias Katzfuss (2024)\n\"Linear-Cost Vecchia Approximation of Multivariate Normal\nProbabilities\" . Two major\nreferences for the development of our method are Alan Genz\n(1992) \"Numerical Computation of Multivariate Normal\nProbabilities\" and Z. I.\nBotev (2017) \"The Normal Law Under Linear Restrictions:\nSimulation and Estimation via Minimax Tilting\"\n.", "License": "GPL (>= 2)", "Config/testthat/edition": "3", "URL": "https://github.com/JCatwood/VeccTMVN", "BugReports": "https://github.com/JCatwood/VeccTMVN/issues", "RoxygenNote": "7.3.2", "Encoding": "UTF-8", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-18 09:44:23 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-02-03 15:40:02 UTC", "RemoteUrl": "https://github.com/cran/VeccTMVN", "RemoteRef": "HEAD", "RemoteSha": "2665ec30d9f9d927a27744583db04eac90173160", "_user": "cran", "_type": "src", "_file": "VeccTMVN_1.3.2.tar.gz", "_fileid": "https://r2.ropensci.org/d5930c66577d4a82b2d14cc7fb73a45c625ae7dff3dece6f446dfe30ca62caff", "_filesize": 148935, "_sha256": "d5930c66577d4a82b2d14cc7fb73a45c625ae7dff3dece6f446dfe30ca62caff", "_expires": "2027-01-26T09:48:59.000Z", "_created": "2026-10-18T09:44:23.000Z", "_published": "2026-10-18T09:49:00.981Z", "_buildurl": "https://github.com/r-universe/cran/actions/runs/39116295776", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 117643448207, "time": 157, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12132218691" }, { "job": 117643448374, "time": 180, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12133070616" }, { "job": 117643448258, "time": 135, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12132641279" }, { "job": 117643448238, "time": 134, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12132780980" }, { "job": 117642724322, "time": 222, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12132657101" }, { "job": 117643448177, "time": 126, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12131959107" }, { "job": 117643448236, "time": 219, "config": "windows-devel-arm64", "r": "4.7.0", 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"mvrandt", "pmvn", "pmvn_MLMC", "pmvt", "pmvt_MLMC", "ptmvrandn", "univar_order", "Vecc_reorder" ], "_help": [ { "page": "FIC_reorder_univar", "title": "Univariate ordering under FIC approximation, first m chosen by m iter of dense univariate reordering", "topics": [ "FIC_reorder_univar" ] }, { "page": "find_nn_corr", "title": "Find ordered nearest neighbors based on a correlation Matrix. Assuming the absolute value of the correlation is monotonically decreasing with distance. Returns an n X (m + 1) matrix similar to `GpGp::find_ordered_nn`.", "topics": [ "find_nn_corr" ] }, { "page": "get_sp_inv_chol", "title": "Get the inverse upper Cholesky factor under the Vecchia approximation", "topics": [ "get_sp_inv_chol" ] }, { "page": "loglk_censor_MVN", "title": "Compute censored multivariate normal (MVN) log-probabilities that have spatial covariance matrices using Vecchia approximation", "topics": [ "loglk_censor_MVN" ] }, { "page": "mvrandn", "title": "Simulate truncated multivariate normal (TMVN) using the Vecchia approximation", "topics": [ "mvrandn" ] }, { "page": "mvrandt", "title": "Simulate truncated multivariate normal (TMVT) using the Vecchia approximation", "topics": [ "mvrandt" ] }, { "page": "pmvn", "title": "Compute multivariate normal (MVN) probabilities that have spatial covariance matrices using Vecchia approximation", "topics": [ "pmvn" ] }, { "page": "pmvn_MLMC", "title": "Applying the multi-level Monte Carlo (MLMC) technique to the pmvn function The function uses 'NLevel1 = 1' for 'm = m2' and the same exponential tilting parameter as 'm = m1' to compute one MC estimate. This MC estimate is used to correct the bias from the Vecchia approximation", "topics": [ "pmvn_MLMC" ] }, { "page": "pmvt", "title": "Compute multivariate Student-t (MVT) probabilities that have spatial covariance matrices using Vecchia approximation", "topics": [ "pmvt" ] }, { "page": "pmvt_MLMC", "title": "Applying the multi-level Monte Carlo (MLMC) technique to the pmvt function The function uses 'NLevel1 = 1' for 'm = m2' and the same exponential tilting parameter as 'm = m1' to compute one MC estimate. This MC estimate is used to correct the bias from the Vecchia approximation", "topics": [ "pmvt_MLMC" ] }, { "page": "ptmvrandn", "title": "Simulate partially censored multivariate normal (MVN) at censored locations using the Vecchia approximation", "topics": [ "ptmvrandn" ] }, { "page": "univar_order", "title": "Univariate variable reordering, described in Genz and Bretz (2009) If failed due to PD singularity, the unfinished order will be returned and a warning will be issued", "topics": [ "univar_order" ] }, { "page": "Vecc_reorder", "title": "Univariate ordering under Vecchia approximation", "topics": [ "Vecc_reorder" ] }, { "page": "VeccTMVN", "title": "VeccTMVN", "topics": [ "VeccTMVN" ] } ], "_rundeps": [ "alabama", "BH", "dotCall64", "fields", "FNN", "GpGp", "GPvecchia", "lattice", "maps", "Matrix", "nleqslv", "numDeriv", "qrng", "RColorBrewer", "Rcpp", "RcppArmadillo", "spacefillr", "spam", "sparseinv", "TruncatedNormal", "truncnorm", "viridisLite" ], "_sysdeps": [ { "shlib": 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