Estimate an auxiliary model based on a latent classification
by means of mixture modeling (see `mx_mixture`

).

The auxiliary model is treated as a multi-group model. All cases are used in all groups, but they are weighted by group-specific BCH weights as described in Bolck, Croon, & Hagenaars, 2004.

`BCH(x, model, data, ...)`

- x
An object for which a method exists.

- model
An object that can be converted to an

`OpenMx`

model using`as_ram`

.- data
A data.frame on which the auxiliary model can be evaluated.

- ...
further arguments to be passed to or from other methods.

An MxModel.

Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3–27. <doi:10.2307/25791751>

```
dat <- data.frame(x = iris$Petal.Length)
mixmod <- mx_profiles(dat,
classes = 2)
#> Running mix2 with 4 parameters
#> Running mix2 with 4 parameters
res <- BCH(mixmod, "y ~ 1", data = data.frame(y = iris$Sepal.Length))
#> Running aux with 4 parameters
```