Normal negative log-likelihood

`nlogL = normlike(params,data)`

[nlogL,AVAR] = normlike(params,data)

[...] = normlike(param,data,censoring)

[...] = normlike(param,data,censoring,freq)

`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.

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