# mvnfit

From networktree v0.2.2
by Payton Jones

##### Maximum Likelihood Estimation for Multivariate Normal Model

Fit a multivariate normal model without covariates or covariance restrictions. In addition to the (straightforward) parameter estimates the fitted log-likelihood and corresponding score contributions are computed.

##### Usage

```
mvnfit(y, x = NULL, start = NULL, weights = NULL, offset = NULL,
model = c("correlation", "mean", "variance"), ..., estfun = FALSE,
object = FALSE)
```

##### Arguments

- y
A matrix or data.frame where each row corresponds to a k-dim observation.

- x
Not used yet

- start
Not used yet

- weights
Not used yet

- offset
Not used yet

- model
Vector of characters. Specifies which estimated parameters are returned.

- ...
Not used yet

- estfun
Logical. Should the matrix of score contributions (aka estimating functions) be returned?

- object
Not used yet

##### Details

Used internally in when method="mob"

*Documentation reproduced from package networktree, version 0.2.2, License: GPL-3*

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