## generate random adjacency matrix
## radjmat <- function (n) {
##     adjmat <- matrix(0L, n, n, dimnames=list(letters[1:n],letters[1:n]))
##     adjmat[lower.tri(adjmat)] <- sample(0:1, n*(n-1)/2, replace=TRUE)
##     adjmat + t(adjmat)
## }
## set.seed(3); adjmat <- radjmat(5)
adjmat <- structure(
    c(0L, 0L, 1L, 0L, 0L,
      0L, 0L, 1L, 1L, 0L,
      1L, 1L, 0L, 0L, 1L,
      0L, 1L, 0L, 0L, 1L,
      0L, 0L, 1L, 1L, 0L),
    .Dim = c(5L, 5L),
    .Dimnames = rep.int(list(c("a", "b", "c", "d", "e")), 2L)
    )

## validated matrix of neighbourhood orders
nbmat <- structure(
    c(0L, 2L, 1L, 3L, 2L,
      2L, 0L, 1L, 1L, 2L,
      1L, 1L, 0L, 2L, 1L,
      3L, 1L, 2L, 0L, 1L,
      2L, 2L, 1L, 1L, 0L),
    .Dim = c(5L, 5L),
    .Dimnames = rep.int(list(c("a", "b", "c", "d", "e")), 2L)
    )

test_that("nbOrder() returns the validated matrix", {
    expect_identical(nbOrder(adjmat),
                     nbmat)
})

test_that("zetaweights(.,maxlag=1,normalize=FALSE) is inverse of nbOrder", {
    expect_identical(zetaweights(nbmat, maxlag=1, normalize=FALSE),
                     1*adjmat)
})