Nothing
context('normal distribution')
test_that('dwnorm returns the correct value for known examples', {
n <- 100
W <- WoodburyMatrix(Diagonal(n), Diagonal(n))
true_with_0 <- -0.5 * (n * log(2 * pi) + n * log(2))
true_with_1 <- true_with_0 - 0.5 * n / 2
expect_equal(
dwnorm(rep(1, n), covariance = W, log = TRUE),
true_with_1
)
expect_equal(
dwnorm(rep(1, n), covariance = W, log = FALSE),
exp(true_with_1)
)
expect_equal(
dwnorm(rbind(rep(0, n), rep(1, n)), covariance = W, log = TRUE),
c(true_with_0, true_with_1)
)
})
test_that('rwnorm return value has correct dimensions', {
n <- 100
check_shapes <- function(W) {
expect_length(rwnorm(1, covariance = W), n)
expect_length(rwnorm(1, 1, W), n)
expect_equal(dim(rwnorm(2, 1, W)), c(2, n))
}
D <- Diagonal(n)
check_shapes(WoodburyMatrix(D, D))
D_dense <- as(as(D, 'symmetricMatrix'), 'denseMatrix')
check_shapes(WoodburyMatrix(D_dense, D_dense))
})
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