Sometimes it's convenient to work with a distribution object that is
akin to a missing value. This is especially true when programmatically
outputting distributions, such as when a distribution fails to fit to
data. This function makes such a distribution object. It always evaluates
to NA.
Details
The Null distribution is the missing value of the distribution world, and
every query about it answers NA in whatever type that query returns:
NA_real_ from mean() and the eval_*() functions, NA_character_ from
vtype(), c(NA, NA) from range(), and no support at all — support()
returns NULL, R's absent-object value. It is also the one distribution
that is.na() finds; see length.dst().
Because of that it is assembled with the package's low-level constructor
rather than through distribution(). A Null distribution cannot satisfy
what distribution() asks of a real one, since it has nothing to declare;
building it here keeps that bypass internal, so a distribution with no
support cannot be made through the front door.
Examples
x <- dst_null()
mean(x)
#> [1] NA
eval_pmf(x, at = 1:10)
#> [1] NA NA NA NA NA NA NA NA NA NA
# It is the distribution that `is.na()` finds.
is.na(x)
#> [1] TRUE
is.na(dst_norm(0, 1))
#> [1] FALSE
# Everything about it is missing, including its support.
vtype(x)
#> [1] NA
range(x)
#> [1] NA NA
support(x)
#> NULL