Add function for taking expectations wrt smoothed weights

[sethaxen]

This PR adds the API function expectation, which allows one to take some expectation wrt to PSIS-smoothed weights. This is the main function needed to compute expectations wrt LOO posteriors, though it is not limited to this application.

Some notes:

• Currently the expectand must have shape (draws, [chains, [params...]]), i.e. it might share the data dimensions. This allows the function to be used for data-dependent scalar expectations e.g. loo_pit. However, one might have data-independent dimensions with or without data dimensions, e.g. for computing the LOO posterior means of all marginals. Supporting this could require a keyword that specifies which (trailing) dims in x are unrelated to the data or draws.
• Currently we do not compute function-specific diagnostics. This requires something like #23, so that we can compute diagnostics without then smoothing.
• Maybe a better way to compute expectations like mean is on the log-scale. However, this would require us to limit which expectations we can support.

Summary by CodeRabbit

• New Features

  • Expanded statistical calculation capabilities with the addition of an expectation function for computing the expectation of values using smoothed importance weights.

• Documentation

  • Updated API documentation to reflect new statistical functions and usage.

• Refactor

  • Integrated the StatsBase module to enhance statistical operations within the module.