## --- The Normal Distribution ----- ##

## S4 method for signature 'DelayedArray'
dnorm(x, mean=0, sd=1, log=FALSE)
## S4 method for signature 'DelayedArray'
pnorm(q, mean=0, sd=1, lower.tail=TRUE, log.p=FALSE)
## S4 method for signature 'DelayedArray'
qnorm(p, mean=0, sd=1, lower.tail=TRUE, log.p=FALSE)

## --- The Binomial Distribution --- ##

## S4 method for signature 'DelayedArray'
dbinom(x, size, prob, log=FALSE)
## S4 method for signature 'DelayedArray'
pbinom(q, size, prob, lower.tail=TRUE, log.p=FALSE)
## S4 method for signature 'DelayedArray'
qbinom(p, size, prob, lower.tail=TRUE, log.p=FALSE)

## --- The Poisson Distribution ---- ##

## S4 method for signature 'DelayedArray'
dpois(x, lambda, log=FALSE)
## S4 method for signature 'DelayedArray'
ppois(q, lambda, lower.tail=TRUE, log.p=FALSE)
## S4 method for signature 'DelayedArray'
qpois(p, lambda, lower.tail=TRUE, log.p=FALSE)

## --- The Logistic Distribution --- ##

## S4 method for signature 'DelayedArray'
dlogis(x, location=0, scale=1, log=FALSE)
## S4 method for signature 'DelayedArray'
plogis(q, location=0, scale=1, lower.tail=TRUE, log.p=FALSE)
## S4 method for signature 'DelayedArray'
qlogis(p, location=0, scale=1, lower.tail=TRUE, log.p=FALSE)

a <- array(4 * runif(1500000), dim=c(10000, 30, 5))
A <- DelayedArray(a)
A

A2 <- dnorm(A + 1)[ , , -3]  # very fast! (operations are delayed)
A2

a2 <- as.array(A2)           # "realize" 'A2' in memory (as an ordinary
                             # array)

DelayedArray(a2) == A2       # DelayedArray object of type "logical"
stopifnot(all(DelayedArray(a2) == A2))


library(HDF5Array)
A3 <- as(A2, "HDF5Array")    # "realize" 'A2' on disk (as an HDF5Array
                             # object)

A3 == A2                     # DelayedArray object of type "logical"
stopifnot(all(A3 == A2))

## See '?DelayedArray' for general information about DelayedArray objects
## and their "realization".