Discrete variables are convoluted with the uniform distribution (see, Nagler,
2017). If a variable should be treated as discrete, declare it as
`ordered()`

.

`kde1d(x, mult = 1, xmin = -Inf, xmax = Inf, bw = NULL, bw_min = 0, ...)`

## Arguments

- x
vector of length \(n\).

- mult
numeric; the actual bandwidth used is \(bw*mult\).

- xmin
lower bound for the support of the density.

- xmax
upper bound for the support of the density.

- bw
bandwidth parameter; has to be a positive number or `NULL`

;
the latter calls `KernSmooth::dpik()`

.

- bw_min
minimum value for the bandwidth.

- ...
unused.

## Value

An object of class `kde1d`

.

## Details

If `xmin`

or `xmax`

are finite, the density estimate will
be 0 outside of \([xmin, xmax]\). Mirror-reflection is used to correct
for boundary bias. Discrete variables are convoluted with the uniform
distribution (see, Nagler, 2017).

## References

Nagler, T. (2017). *A generic approach to nonparametric function
estimation with mixed data.* arXiv:1704.07457

## Examples

```
data(wdbc, package = "kdecopula") # load data
fit <- kde1d(wdbc[, 5]) # estimate density
dkde1d(1000, fit) # evaluate density estimate
#> [1] 0.0003689227
```