{ "_id": "6a4881c6e06c18f0a653e31c", "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", "Repository": "https://jcatwood.r-universe.dev", "Date/Publication": "2026-01-27 03:49:45 UTC", "RemoteUrl": "https://github.com/jcatwood/vecctmvn", "RemoteRef": "HEAD", "RemoteSha": "1401f57436365a8698eb2a9f7330186dee759317", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-04 03:39:52 UTC", "User": "root" }, "_user": "jcatwood", "_type": "src", "_file": "VeccTMVN_1.3.2.tar.gz", "_fileid": "https://r2.ropensci.org/77e089ef3ffc14998ec652c42e4c7e90f417ca5ffaee811045b5fd88fc127d96", "_filesize": 151385, "_sha256": "77e089ef3ffc14998ec652c42e4c7e90f417ca5ffaee811045b5fd88fc127d96", "_expires": "2026-10-12T03:45:09.000Z", "_created": "2026-07-04T03:39:52.000Z", "_published": "2026-07-04T03:45:10.913Z", "_jobs": [ { "job": 85099881432, "time": 165, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "8077983855" }, { "job": 85099881442, "time": 151, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8077982006" }, { "job": 85099881425, "time": 158, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "8077983086" }, { "job": 85099881466, "time": 150, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8077981928" }, { "job": 85099881424, "time": 187, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "8077982091" }, { "job": 85099881417, "time": 257, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "8077991686" }, { "job": 85099881426, "time": 159, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "8077979627" }, { "job": 85099881423, "time": 219, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8077988018" }, { "job": 85099667378, "time": 211, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8077965058" }, { "job": 85099881431, "time": 184, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8077985705" }, { "job": 85099881420, "time": 143, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "8077981196" }, { "job": 85099881414, "time": 142, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "8077980964" }, { "job": 85099881418, "time": 144, "config": "windows-release", "r": "4.6.1", "check": "OK", "artifact": "8077981259" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/jcatwood/actions/runs/28693725758", "_status": "success", "_upstream": "https://github.com/jcatwood/vecctmvn", "_commit": { "id": "1401f57436365a8698eb2a9f7330186dee759317", "author": "JCatwood ", "committer": "JCatwood ", "message": "Add guards to check if n > m. If not, error will be raised.\n", "time": 1769485785 }, "_maintainer": { "name": "Jian Cao", "email": "jcao2416@gmail.com", "login": "jcatwood", "description": "", "uuid": 12179092 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "Rcpp", "version": ">= 1.0.10", "role": "Imports" }, { "package": "Matrix", "version": ">= 1.5-3", "role": "Imports" }, { "package": "GpGp", "version": ">= 0.4.0", "role": "Imports" }, { "package": "truncnorm", "version": ">= 1.0-8", "role": "Imports" }, { "package": "GPvecchia", "role": "Imports" }, { "package": "TruncatedNormal", "role": "Imports" }, { "package": "nleqslv", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "lhs", "role": "Suggests" }, { "package": "mvtnorm", "role": "Suggests" } ], "_owner": "jcatwood", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-27", "n": 1 }, { "week": "2025-28", "n": 1 }, { "week": "2025-29", "n": 3 }, { "week": "2025-34", "n": 1 }, { "week": "2026-05", "n": 1 } ], "_tags": [ { "name": "v1.3.0", "date": "2025-07-14" }, { "name": "V1.3.1", "date": "2025-08-19" } ], "_topics": [ "normal-distribution", "sampling-methods", "statistics", "openblas", "cpp", "openmp" ], "_stars": 2, "_contributors": [ { "user": "jcatwood", "count": 11, "uuid": 12179092 }, { "user": "olivroy", "count": 1, "uuid": 52606734 } ], "_userbio": { "uuid": 12179092, "type": "user", "name": "Jian Cao", "followers": 10 }, "_downloads": { "count": 298, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/VeccTMVN" }, "_devurl": "https://github.com/jcatwood/vecctmvn", "_searchresults": 29, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/VeccTMVN.html", "manual.pdf" ], "_homeurl": "https://github.com/jcatwood/vecctmvn", "_realowner": "jcatwood", "_cranurl": true, "_releases": [ { "version": "1.0.0", "date": "2024-01-26" }, { "version": "1.1.0", "date": "2024-08-16" }, { "version": "1.1.1", "date": "2024-09-04" }, { "version": "1.2.0", "date": "2024-09-23" }, { "version": "1.2.1", "date": "2024-11-26" }, { "version": "1.3.0", "date": "2025-07-14" }, { "version": "1.3.1", "date": "2025-08-19" }, { "version": "1.3.2", "date": "2026-02-03" } ], "_exports": [ "FIC_reorder_univar", "find_nn_corr", "get_sp_inv_chol", "loglk_censor_MVN", "mvrandn", "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" ] } ], "_readme": "https://github.com/jcatwood/vecctmvn/raw/HEAD/README.md", "_rundeps": [ "alabama", "BH", "dotCall64", "fields", "FNN", "GpGp", "GPvecchia", "lattice", "maps", "Matrix", "nleqslv", "numDeriv", "qrng", "RColorBrewer", "Rcpp", "RcppArmadillo", "spacefillr", "spam", "sparseinv", 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