Weekly Rt with autoregressive difference

[gvegayon]

This PR adds the following:

• [x] A pair of classes for the process module: RtPeriodicDiffProcess and RtWeeklyDiffProcess(RtPeriodicDiffProcess) (an instance).
  • The RandomVariable samples from an autoregressive process of the following form: $\log Rt(t) \sim Normal(\log Rt(t - 1) + \beta (\log Rt(t-1) - \log Rt(t-2)), \sigma^2_r)$
  • The sampled Rt is then extended to match the length of the data.
  • The class allows starting from any day of the week.
  • $\beta$, $\sigma$, and the initialization of Rt (Rt(0) and Rt(1)) are provided as RandomVariables.
• [x] Adds tests validating the behavior of the class.

[codecov[bot]]

Codecov Report

Attention: Patch coverage is 97.01493% with 2 lines in your changes missing coverage. Please review.

Project coverage is 94.29%. Comparing base (394a03d) to head (1af7ba5). Report is 1 commits behind head on main.

Files	Patch %	Lines
...odel/src/pyrenew/model/rtinfectionsrenewalmodel.py	87.50%	1 Missing :warning:
model/src/pyrenew/process/rtperiodicdiff.py	97.87%	1 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #131 +/- ## ========================================== + Coverage 93.93% 94.29% +0.35% ========================================== Files 36 37 +1 Lines 709 771 +62 ========================================== + Hits 666 727 +61 - Misses 43 44 +1 ``` | [Flag](https://app.codecov.io/gh/CDCgov/multisignal-epi-inference/pull/131/flags?src=pr&el=flags&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) | Coverage Δ | | |---|---|---| | [unittests](https://app.codecov.io/gh/CDCgov/multisignal-epi-inference/pull/131/flags?src=pr&el=flag&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov) | `94.29% <97.01%> (+0.35%)` | :arrow_up: | Flags with carried forward coverage won't be shown. [Click here](https://docs.codecov.io/docs/carryforward-flags?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=CDCgov#carryforward-flags-in-the-pull-request-comment) to find out more.

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[gvegayon]

Working on the conflicts

[gvegayon]

Ready for review, @dylanhmorris and @damonbayer .

[gvegayon]

Looks good, but I had one question about duration. Why did we opt for this instead of n_timepoints? They appear to represent the same concept, but maybe I am missing something.

I am a bit fuzzy about this. The duration keyword is used for processes only, while n_timepoints for models. I don't have strong feelings about it, but duration seemed like a natural name for processes, so I didn't want to change that. But the number of observations in a model didn't sound like duration, so I opted for using n_timepoints. Perhaps this could be homogenized to length or something? I suggest continuing this discussion in an issue.

[dylanhmorris]

Happy to standardize on n_timepoints. Probably a separate PR.