# Documentation

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# normlike

Normal negative log-likelihood

## Syntax

```nlogL = normlike(params,data) [nlogL,AVAR] = normlike(params,data) [...] = normlike(param,data,censoring) [...] = normlike(param,data,censoring,freq) ```

## Description

`nlogL = normlike(params,data)` returns the negative of the normal log-likelihood function. `params(1)` is the mean, `mu`, and `params(2)` is the standard deviation, `sigma`.

`[nlogL,AVAR] = normlike(params,data)` also returns the inverse of Fisher's information matrix, `AVAR`. If the input parameter values in `params` are the maximum likelihood estimates, the diagonal elements of `AVAR` are their asymptotic variances. `AVAR` is based on the observed Fisher's information, not the expected information.

`[...] = normlike(param,data,censoring)` accepts a Boolean vector, `censoring`, of the same size as `data`, which is 1 for observations that are right-censored and 0 for observations that are observed exactly.

`[...] = normlike(param,data,censoring,freq)` accepts a frequency vector, `freq`, of the same size as `data`. The vector `freq` typically contains integer frequencies for the corresponding elements in `data`, but can contain any nonnegative values. Pass in `[]` for `censoring` to use its default value.

`normlike` is a utility function for maximum likelihood estimation.