Minor fixes to `quantile(<dist_mixture>)` and `cdf(<dist_sample>)`

[mjskay]

Two minor bugfixes for some issues I ran into when trying to improve support for visualizing discrete distributions in {ggdist}:

• quantile(<dist_mixture>) gives weird results on discrete mixtures. For example, on master:

x <- dist_mixture(dist_degenerate(1), dist_degenerate(2), dist_degenerate(3), weights = c(0.1, 0.2, 0.7))
quantile(x, c(0, 0.1, 0.3, 1))[[1]]
#> [[1]]
#> [1] 1.000000 1.472202 2.236134 3.000000

This should be 1 1 2 3. This PR changes the implementation of quantile.dist_mixture() to use uniroot() instead of optimise(), which seems to fix the problem (at least, to within the default tolerance of .Machine$double.eps^0.25):

quantile(x, c(0, 0.1, 0.3, 1))
#> [[1]]
#> [1] 1.000000 1.000000 2.000034 3.000000

• cdf(<dist_sample>) is currently defined as $P(X < x)$ rather than $P(X \le x)$. I believe it should be the latter (which would also be consistent with ecdf()). For example, on master:

cdf(dist_sample(list(2)), 2)
#> x 
#> 0 

This should be 1. This PR changes the implementation of cdf.dist_sample() accordingly:

cdf(dist_sample(list(2)), 2)
#> x 
#> 1

Tests are included for both fixes. There is also a minor fix to format(<dist_mixture>), though it didn't seem newsworthy to me.

[mitchelloharawild]

Great, thanks!