Summary

This major version release brings several new features and API changes to improve usability, and accommodate demography-only analyses.

Inference for demography and mush is now done with two independent methods: kSFS.infer_eta and kSFS.infer_mush. See API docs for details.
Inference of ancestral state misidentification rate, for both sample frequency and mutation type, obviates frequency masking, which has been removed.
More interpretable model selection based on trend penalties. A user can supply as many trend penalties as they wish. A trend penalty of order k will encourage order k polynomial pieces in the solution (e.g. k=0 for piecewise constant). Trend penalties of different orders can be combined to get mixed trends. See API docs on the the inference methods above for details.
Simplified simulation notebook and updated Quickstart (see docs).

Under the hood

Rewrite of mushi.optimization module with abstraction and inheritance to avoid a lot of duplicated code. Added a trend filtering optimizer class based on the recursive ADMM of Ramdas and Tibshirani (this serves as the prox operator in the outer optimization routine when fitting demography or mush). I was able to get this running quite fast by caching Cholesky decompositions and using the fast prox-tv module for dual variable updates. I find that about 20 iterations of ADMM are plenty (although the default is 100).
Consolidated SFS folding code into a function in the utils model
Introduced a frequency misidentification operator and a mutation type misidentification operator to our model of the expected kSFS, and misidentification rate r that is a learned parameter for eta inference, and a vector of rates for MuSH inference.
It is no longer necessary to specify the time grid for inference. If no grid is specified, a reasonable default grid is constructed based on the TMRCA under a constant history. To specify the grid, use the parameters pts and ta when running kSFS.infer_eta().
Dedicated loss function model mushi.loss_functions. Inference can be done using any loss function from this module.