dbifgmcop(x1, x2, apar, log = FALSE)
pbifgmcop(q1, q2, apar)
rbifgmcop(n, apar)
runif
.TRUE
then the logarithm is returned.dbifgmcop
gives the density,
pbifgmcop
gives the distribution function, and
rbifgmcop
generates random deviates (a two-column matrix).bifgmcop
, the bifgmcop
.N <- 101; x <- seq(0.0, 1.0, len = N); apar <- 0.7
ox <- expand.grid(x, x)
zedd <- dbifgmcop(ox[, 1], ox[, 2], apar = apar)
contour(x, x, matrix(zedd, N, N), col = "blue")
zedd <- pbifgmcop(ox[, 1], ox[, 2], apar = apar)
contour(x, x, matrix(zedd, N, N), col = "blue")
plot(r <- rbifgmcop(n = 3000, apar = apar), col = "blue")
par(mfrow = c(1, 2))
hist(r[, 1]) # Should be uniform
hist(r[, 2]) # Should be uniform
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