Absorb parameter values into sparsity-type/smoothness-type specification about moma

[michaelweylandt]

clustRviz is here: https://github.com/DataSlingers/clustRviz

The weights argument there can be two things: either a custom weight matrix or a function which is called on the data to build a weight matrix for that specific problem. We'd definitely want to do something similar because there are times when non-standard smoothing matrices make sense (e.g., in the SFPCA paper, we use great circle distances for points on the cortical surface) but it's worth it to make a convenient shortcut for the 95% case of second difference matrices.

[Banana1530]

Summarizing, the ideal solution to this issue consists of two parts:

1. create argument functions for smoothness type; and
2. put parameter values into argument functions.

How clustRviz does this is clever, using tricks in functional programming.

https://github.com/DataSlingers/clustRviz/blob/93f56ab88675656a81f2bcd13a96e7ff181551b2/R/weights.R#L77

[Banana1530]

A tentative solution is here:

Banana1530/MoMA-1@a4b64db

(currently broken due to code formatting issues.)