## Bayesian inference for a normal standard deviation with a scaled inverse chi-squared distribution

### Description

Evaluates and plots the posterior density for sigma, the standard deviation of a Normal distribution where the mean mu is known

### Usage

```nvaricp(y, mu, S0, kappa, ...)
```

### Arguments

 `y` a random sample from a normal(mu,sigma^2) distribution. `mu` the known population mean of the random sample. `S0` the prior scaling factor. `kappa` the degrees of freedom of the prior. `...` additional arguments that are passed to `Bolstad.control`

### Value

A list will be returned with the following components:

 `sigma` the vaules of sigma for which the prior, likelihood and posterior have been calculated `prior` the prior density for sigma `likelihood` the likelihood function for sigma given y `posterior` the posterior density of sigma given y `S1` the posterior scaling constant `kappa1` the posterior degrees of freedom

### Examples

```
## Suppose we have five observations from a normal(mu, sigma^2)
## distribution mu = 200 which are 206.4, 197.4, 212.7, 208.5.
y = c(206.4, 197.4, 212.7, 208.5, 203.4)

## We wish to choose a prior that has a median of 8. This happens when
## S0 = 29.11 and kappa = 1
nvaricp(y,200,29.11,1)

##  Same as the previous example but a calculate a 95% credible
## interval for sigma. NOTE this method has changed
results = nvaricp(y,200,29.11,1)
quantile(results, probs = c(0.025, 0.975))
```