Series statistics:

Functions such as `count`, `mean`, `kurt` etc. return the statistics calculated over all values of a series. The calculation skips over missing values (or `nan` values), so for example `mean` returns the average of all _present_ values.

Frame statistics:

The standard functions are exposed as static members and are overloaded. This means that they can be applied to both `Series<'K, float>` and to `Frame<'R, 'C>`. When applied to data frame, the functions apply the statistical calculation to all numerical columns of the frame.

Moving windows:

Moving window means that the window has a fixed size and moves over the series. In this case, the result of the statisitcs is always attached to the last key of the window. The function names are prefixed with `moving`.

Expanding windows:

Expanding window means that the window starts as a single-element sized window and expands as it moves over the series. In this case, statistics is calculated for all values up to the current key. This means that the result is attached to the key at the end of the window. The function names are prefixed with `expanding`.

Multi-level statistics:

For a series with multi-level (hierarchical) index, the functions prefixed with `level` provide a way to apply statistical operation on a single level of the index. (For example you can sum values along the `'K1` keys in a series `Series<'K1 * 'K2, float>` and get `Series<'K1, float>` as the result.)

The windowing functions in the `Stats` type support calculations over a fixed-size windows specified by the size of the window. If you need more complex windowing behavior (such as window based on the distance between keys), different handling of boundary, or chunking (calculation over adjacent chunks), you can use chunking and windowing functions from the `Series` module such as `Series.windowSizeInto` or `Series.chunkSizeInto`.