## Active bindings

`clusters`

vector indicating the number of clusters considered is the successively fitted models

## Methods

## Inherited methods

### Method `new()`

helper function for forward smoothing: split a group

Initialize all models in the collection.

#### Usage

```
PLNmixturefamily$new(
clusters,
responses,
covariates,
offsets,
formula,
control
)
```

#### Arguments

`clusters`

the dimensions of the successively fitted models

`responses`

the matrix of responses common to every models

`covariates`

the matrix of covariates common to every models

`offsets`

the matrix of offsets common to every models

`formula`

model formula used for fitting, extracted from the formula in the upper-level call

`control`

a list for controlling the optimization. See details.

`control`

a list for controlling the optimization. See details.

Call to the optimizer on all models of the collection

#### Usage

`PLNmixturefamily$optimize(config)`

#### Arguments

`config`

a list for controlling the optimization

function to restart clustering to avoid local minima by smoothing the loglikelihood values as a function of the number of clusters

#### Usage

`PLNmixturefamily$smooth(control)`

#### Arguments

`control`

a list to control the smoothing process

Lineplot of selected criteria for all models in the collection

#### Usage

`PLNmixturefamily$plot(criteria = c("loglik", "BIC", "ICL"), reverse = FALSE)`

#### Arguments

`criteria`

A valid model selection criteria for the collection of models. Any of "loglik", "BIC" or "ICL" (all).

`reverse`

A logical indicating whether to plot the value of the criteria in the "natural" direction
(loglik - 0.5 penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the
natural direction, on the same scale as the log-likelihood..

### Method `plot_objective()`

Plot objective value of the optimization problem along the penalty path

#### Usage

`PLNmixturefamily$plot_objective()`

Extract best model in the collection

#### Usage

`PLNmixturefamily$getBestModel(crit = c("BIC", "ICL", "loglik"))`

#### Arguments

`crit`

a character for the criterion used to performed the selection. Either
"BIC", "ICL" or "loglik". Default is `ICL`

### Method `show()`

User friendly print method

User friendly print method

### Method `clone()`

The objects of this class are cloneable with this method.

#### Usage

`PLNmixturefamily$clone(deep = FALSE)`

#### Arguments

`deep`

Whether to make a deep clone.