Calculate the model-implied indicator or construct variance-covariance (VCV) matrix. Currently only the model-implied VCV for recursive linear models is implemented (including models containing second order constructs).

```
fit(
.object = NULL,
.saturated = args_default()$.saturated,
.type_vcv = args_default()$.type_vcv
)
```

- .object
An R object of class cSEMResults resulting from a call to

`csem()`

.- .saturated
Logical. Should a saturated structural model be used? Defaults to

`FALSE`

.- .type_vcv
Character string. Which model-implied correlation matrix should be calculated? One of "

*indicator*" or "*construct*". Defaults to "*indicator*".

Either a (K x K) matrix or a (J x J) matrix depending on the `type_vcv`

.

Notation is taken from Bollen (1989)
.
If `.saturated = TRUE`

the model-implied variance-covariance matrix is calculated
for a saturated structural model (i.e., the VCV of the constructs is replaced
by their correlation matrix). Hence: V(eta) = WSW' (possibly disattenuated).

Bollen KA (1989).
*Structural Equations with Latent Variables*.
Wiley-Interscience.
ISBN 978-0471011712.