Further review on `cdc_baseline_forecaster`

`cdc_baseline_forcecaster` can successfully reproduce historical Flusight-baseline forecasts from last season; see [this script](https://github.com/cmu-delphi/forecasting-team-notebooks/blob/main/flusight-baseline/flusight-baseline-2023-24.R). However, some extra features in `cdc_baseline_forecaster` that aren't needed to produce Flusight-baseline forecasts may need some extra review.

[also review claims re. covid if they still exist]

Rough points to revisit; some probably don't actually apply

- check if runs when doing geo pooling
- .$ in epi_slide… prefer .x$? or fine?
- locale-independent Saturday check?
- styler
- .data$ / .env$ to calm checks? or fn Nat used in epiprocess?
- why !!outcome?
- why predictor on keys? could this be follow "id variable" example in \`?add<sub>role</sub>\`?
- (1group_by(across(……))1 vs. 1group_by(pick(…….))1? deprecation planned in future . but for compatibility better to keep around??)
- missing a step_epinaomit for the training window? (but what about test data selection?)
- side
- \`get<sub>testdata</sub> \`arg validation & fixup a little weird looking (might be able to combine some, \`allow<sub>null</sub>=TRUE\` inside non-NULL branch, -Inf thing weird, class != class)
- ~ min(.x$lag %||% Inf) — why min? also, mapping across all steps…. need to remember this if doing archive-based recipes b/c we may not want this
- check… max lags & max horizon also might not be archive-backcast-compatible unless already doing transform to epi\`_df
- what??\`?

if (is.null(n_recent)) n_recent <- min_required + 1 # one extra for filling
if (n_recent <= min_required) n_recent <- min_required + n_recent

- appears to be flatline + iterated (as if independent) symmetrized 1-week
differences, separately for each geo (w/ no time window, no transformation)
- no need for \`if (args<sub>list</sub>$nonneg) f <- layer<sub>threshold</sub>(f, ".pred")\`? or does it need to be before \`cdc<sub>flatlinequantiles</sub>\`? or not?
- what type of warning are we trying to suppress with suppressWarnings? be more selective?
- incomplete propagate test
- major
- \`data<sub>frequenc</sub>\`y not considered in layer?
- check on hhs… we don't want filling through forecast_date
- nsims much smaller?
- do we really want warning + something different rather than error when
\`by<sub>key</sub>\` cols aren't available? also, the warnings don't trigger?? probably
just casualty of suppressWarnings
- no clue about the reasoning here

> \`nafill<sub>buffer</sub>\`: At predict time, recent values of the training data are
> used to create a forecast. However, these can be 'NA' due to,
> e.g., data latency issues. By default, any missing values
> will get filled with less recent data. Setting this value to
> 'NULL' will result in 1 extra recent row (beyond those
> required for lag creation) to be used. Note that we require
> at least 'min(lags)' rows of recent data per '\`geo<sub>value</sub>\`' to
> create a prediction. For this reason, setting 'nafill_buffer
> < min(lags)' will be treated as additional allowed recent
> data rather than the total amount of recent data to examine.

- semimajor
- assume this doesn't actually work with \`time<sub>type</sub>\` = week?
- data frequency was not 1 week for covid-19 forecasts
- need to do a \`step<sub>epilag</sub>\` 1? data_frequency? to get the right training window selection?
- if there are gaps, are deltas appropriately NA?
- with hhs latency, \`forecast<sub>date</sub>\` & \`target<sub>date</sub>\` setting is awkward
- for non-flatline, had issue with contrasts on 1 geo and with residuals not matching size on mult geos. maybe missing \`step<sub>epinaomit</sub>\`? but adding \`step<sub>epinaomit</sub>\` gives \`Warning: Values from \`q\` are not uniquely identified; output will contain list-cols.\`
- awkward…:

if (max_ahead > 1L) {
for (iter in 2:max_ahead) {

- filter to Saturdays & no time_type update…
- \`epiprocess::guess<sub>period</sub>\` useful?
- \`state<sub>census</sub>$fips\` should be chr
- maybe avoid \`sample\` due to length-1-numeric case? unlikely to encounter but bad…