Peirce's criterion and Chauvenet's criterion were both proposed in the 1800s
as a way of determining what observations should be rejected in a univariate sample.

## Usage

```
peirce_anomalies(y)
chauvenet_anomalies(y)
```

## Arguments

- y
numerical vector of observations

## Details

These functions take a univariate sample `y`

and return a logical
vector indicating which observations should be considered anomalies according
to either Peirce's criterion or Chauvenet's criterion.

## References

Peirce, B. (1852). Criterion for the rejection of doubtful observations.
*The Astronomical Journal*, 2(21), 161–163.

Chauvenet, W. (1863). 'Method of least squares'. Appendix to
*Manual of Spherical and Practical Astronomy*, Vol.2, Lippincott, Philadelphia, pp.469-566.

## Examples

```
y <- rnorm(1000)
tibble(y = y) |> filter(peirce_anomalies(y))
#> # A tibble: 1 × 1
#> y
#> <dbl>
#> 1 -3.83
tibble(y = y) |> filter(chauvenet_anomalies(y))
#> # A tibble: 1 × 1
#> y
#> <dbl>
#> 1 -3.83
```