Multivariate Normal Probabilities using Vecchia Approximation


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Documentation for package ‘VeccTMVN’ version 1.2.1

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FIC_reorder_univar Univariate ordering under FIC approximation, first m chosen by m iter of dense univariate reordering
find_nn_corr 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'.
get_sp_inv_chol Get the inverse upper Cholesky factor under the Vecchia approximation
loglk_censor_MVN Compute censored multivariate normal (MVN) log-probabilities that have spatial covariance matrices using Vecchia approximation
mvrandn Simulate truncated multivariate normal (TMVN) using the Vecchia approximation
mvrandt Simulate truncated multivariate normal (TMVT) using the Vecchia approximation
pmvn Compute multivariate normal (MVN) probabilities that have spatial covariance matrices using Vecchia approximation
pmvn_MLMC 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
pmvt Compute multivariate Student-t (MVT) probabilities that have spatial covariance matrices using Vecchia approximation
pmvt_MLMC 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
ptmvrandn Simulate partially censored multivariate normal (MVN) at censored locations using the Vecchia approximation
univar_order 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
VeccTMVN VeccTMVN
Vecc_reorder Univariate ordering under Vecchia approximation