This function is a wrapper around `mx_mixture`

to simplify the
specification of latent profile models, also known as finite mixture models.
By default, the function estimates free means for all observed variables
across classes.

```
mx_profiles(
data = NULL,
classes = 1L,
variances = "equal",
covariances = "zero",
run = TRUE,
expand_grid = FALSE,
...
)
```

- data
The data.frame to be used for model fitting.

- classes
A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g.,

`classes = 1:6`

,- variances
Character vector. Specifies which variance components to estimate. Defaults to "equal" (constrain variances across classes); the other option is "varying" (estimate variances freely across classes). Each element of this vector refers to one of the models you wish to run.

- covariances
Character vector. Specifies which covariance components to estimate. Defaults to "zero" (covariances constrained to zero; this corresponds to an assumption of conditional independence of the indicators); other options are "equal" (covariances between items constrained to be equal across classes), and "varying" (free covariances across classes).

- run
Logical, whether or not to run the model. If

`run = TRUE`

, the function calls`mixture_starts`

and`run_mx`

.- expand_grid
Logical, whether or not to estimate all possible combinations of the

`variances`

and`covariances`

arguments. Defaults to`FALSE`

.- ...
Additional arguments, passed to functions.

Returns an `mxModel`

.

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

```
if (FALSE) {
data("empathy")
df <- empathy[1:6]
mx_profiles(data = df,
classes = 2) -> res
}
```