Illustrated probability calculations from distributions

```
pdist(
dist = "norm",
q,
plot = TRUE,
verbose = FALSE,
invisible = FALSE,
digits = 3L,
xlim,
ylim,
resolution = 500L,
return = c("values", "plot"),
...,
refinements = list()
)
xpgamma(
q,
shape,
rate = 1,
scale = 1/rate,
lower.tail = TRUE,
log.p = FALSE,
...
)
xpt(q, df, ncp, lower.tail = TRUE, log.p = FALSE, ...)
xpchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)
xpf(q, df1, df2, lower.tail = TRUE, log.p = FALSE, ...)
xpbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE, ...)
xppois(q, lambda, lower.tail = TRUE, log.p = FALSE, ...)
xpgeom(q, prob, lower.tail = TRUE, log.p = FALSE, ...)
xpnbinom(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE, ...)
xpbeta(q, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)
```

- dist
a character description of a distribution, for example

`"norm"`

,`"t"`

, or`"chisq"`

- q
a vector of quantiles

- plot
a logical indicating whether a plot should be created

- verbose
a logical

- invisible
a logical

- digits
the number of digits desired

- xlim
x limits

- ylim
y limits

- resolution
Number of points used for detecting discreteness and generating plots. The default value of 5000 should work well except for discrete distributions that have many distinct values, especially if these values are not evenly spaced.

- return
If

`"plot"`

, return a plot. If`"values"`

, return a vector of numerical values.- ...
Additional arguments, typically for fine tuning the plot.

- refinements
A list of refinements to the plot. See

`ggformula::gf_refine()`

.- shape, scale
shape and scale parameters. Must be positive,

`scale`

strictly.- rate
an alternative way to specify the scale.

- lower.tail
logical; if TRUE (default), probabilities are \(P[X \le x]\), otherwise, \(P[X > x]\).

- log.p
A logical indicating whether probabilities should be returned on the log scale.

- df
degrees of freedom (\(> 0\), maybe non-integer).

`df = Inf`

is allowed.- ncp
non-centrality parameter \(\delta\); currently except for

`rt()`

, only for`abs(ncp) <= 37.62`

. If omitted, use the central t distribution.- df1, df2
degrees of freedom.

`Inf`

is allowed.- size
number of trials (zero or more).

- prob
probability of success on each trial.

- lambda
vector of (non-negative) means.

- mu
alternative parametrization via mean: see ‘Details’.

- shape1, shape2
non-negative parameters of the Beta distribution.

A vector of probabilities; a plot is printed as a side effect.

The most general function is `pdist`

which can work with
any distribution for which a p-function exists. As a convenience, wrappers are
provided for several common distributions.

```
pdist("norm", -2:2)
#> [1] 0.02275013 0.15865525 0.50000000 0.84134475 0.97724987
pdist("norm", seq(80,120, by = 10), mean = 100, sd = 10)
#> [1] 0.02275013 0.15865525 0.50000000 0.84134475 0.97724987
pdist("chisq", 2:4, df = 3)
#> [1] 0.4275933 0.6083748 0.7385359
pdist("f", 1, df1 = 2, df2 = 10)
#> [1] 0.5981224
pdist("gamma", 2, shape = 3, rate = 4)
#> [1] 0.986246
```