Generic function for computing probability density function (PDF) contributions based on a distribution object and observed data.

## Arguments

- d
An object. The package provides methods for distribution objects such as those from

`Normal()`

or`Binomial()`

etc.- x
A vector of elements whose probabilities you would like to determine given the distribution

`d`

.- drop
logical. Should the result be simplified to a vector if possible?

- ...
Arguments passed to methods. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

## Details

The generic function `pdf()`

computes the probability density,
both for continuous and discrete distributions. `pmf()`

(for the
probability mass function) is an alias that just calls `pdf()`

internally.
For computing log-density contributions (e.g., to a log-likelihood)
either `pdf(..., log = TRUE)`

can be used or the generic function
`log_pdf()`

.

## Examples

```
## distribution object
X <- Normal()
## probability density
pdf(X, c(1, 2, 3, 4, 5))
#> [1] 2.419707e-01 5.399097e-02 4.431848e-03 1.338302e-04 1.486720e-06
pmf(X, c(1, 2, 3, 4, 5))
#> [1] 2.419707e-01 5.399097e-02 4.431848e-03 1.338302e-04 1.486720e-06
## log-density
pdf(X, c(1, 2, 3, 4, 5), log = TRUE)
#> [1] -1.418939 -2.918939 -5.418939 -8.918939 -13.418939
log_pdf(X, c(1, 2, 3, 4, 5))
#> [1] -1.418939 -2.918939 -5.418939 -8.918939 -13.418939
```