S4 class for standard deviation. It inherits from EL class.

## Slots

`optim`

A list of the following optimization results:

`par`

A numeric vector of the specified parameters.`lambda`

A numeric vector of the Lagrange multipliers of the dual problem corresponding to`par`

.`iterations`

A single integer for the number of iterations performed.`convergence`

A single logical for the convergence status.`cstr`

A single logical for whether constrained EL optimization is performed or not.

`logp`

A numeric vector of the log probabilities of the empirical likelihood.

`logl`

A single numeric of the empirical log-likelihood.

`loglr`

A single numeric of the empirical log-likelihood ratio.

`statistic`

A single numeric of minus twice the empirical log-likelihood ratio with an asymptotic chi-square distribution.

`df`

A single integer for the degrees of freedom of the statistic.

`pval`

A single numeric for the \(p\)-value of the statistic.

`nobs`

A single integer for the number of observations.

`npar`

A single integer for the number of parameters.

`weights`

A numeric vector of the re-scaled weights used for the model fitting.

`coefficients`

A numeric vector of the maximum empirical likelihood estimates of the parameters.

`method`

A single character for the method dispatch in internal functions.

`data`

A numeric matrix of the data for the model fitting.

`control`

An object of class ControlEL constructed by

`el_control()`

.

## Examples

```
showClass("SD")
#> Class "SD" [package "melt"]
#>
#> Slots:
#>
#> Name: optim logp logl loglr statistic
#> Class: list numeric numeric numeric numeric
#>
#> Name: df pval nobs npar weights
#> Class: integer numeric integer integer numeric
#>
#> Name: coefficients method data control
#> Class: numeric character ANY ControlEL
#>
#> Extends: "EL"
```