Fast computation of the multivariate normal density.

Fast computation of the multivariate normal density.

dmvn(X, mu, sigma, log = FALSE, ncores = 1, isChol = FALSE)

Arguments

X	matrix n by d where each row is a d dimensional random vector. Alternatively X can be a d-dimensional vector.
mu	vector of length d, representing the mean of the distribution.
sigma	covariance matrix (d x d). Alternatively it can be the cholesky decomposition of the covariance. In that case isChol should be set to TRUE.
log	boolean set to true the logarithm of the pdf is required.
ncores	Number of cores used. The parallelization will take place only if OpenMP is supported.
isChol	boolean set to true is sigma is the cholesky decomposition of the covariance matrix.

Value

A vector of length n where the i-the entry contains the pdf of the i-th random vector.

Examples

# NOT RUN {
N <- 100
d <- 5
mu <- 1:d
X <- t(t(matrix(rnorm(N*d), N, d)) + mu)
tmp <- matrix(rnorm(d^2), d, d)
mcov <- tcrossprod(tmp, tmp)  + diag(0.5, d)
myChol <- chol(mcov)

head(dmvn(X, mu, mcov), 10)
head(dmvn(X, mu, myChol, isChol = TRUE), 10)

# }# NOT RUN {
# Performance comparison
library(mvtnorm)
library(microbenchmark)

a <- cbind(
      dmvn(X, mu, mcov),
      dmvn(X, mu, myChol, isChol = TRUE),
      dmvnorm(X, mu, mcov))

# Check if we get the same output as dmvnorm()
a[ , 1] / a[, 3]
a[ , 2] / a[, 3]

microbenchmark(dmvn(X, mu, myChol, isChol = TRUE),
               dmvn(X, mu, mcov),
               dmvnorm(X, mu, mcov))

detach("package:mvtnorm", unload=TRUE)
# }# NOT RUN {
# }