Skip to content

VeccTMVN

Multivariate Normal Probabilities using Vecchia Approximation

v1.3.2 · Feb 3, 2026 · GPL (>= 2)

Description

Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.

Downloads

224

Last 30 days

20453rd

710

Last 90 days

2.3K

Last year

Trend: -30% (30d vs prior 30d)

CRAN Check Status

14 OK
Show all 14 flavors
Flavor Status
r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK

Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies Rcpp Matrix GpGp truncnorm GPvecchia TruncatedNormal nleqslv VeccTMVN

Version History

new 1.3.2 Mar 10, 2026
updated 1.3.2 ← 1.3.1 diff Feb 2, 2026
updated 1.3.1 ← 1.3.0 diff Aug 18, 2025
updated 1.3.0 ← 1.2.1 diff Jul 13, 2025
updated 1.2.1 ← 1.2.0 diff Nov 25, 2024
updated 1.2.0 ← 1.1.1 diff Sep 22, 2024
updated 1.1.1 ← 1.1.0 diff Sep 4, 2024
updated 1.1.0 ← 1.0.0 diff Aug 15, 2024
new 1.0.0 Jan 25, 2024