Implement censored families

[tomicapretto]

This PR implements censored families. For example

model = bmb.Model(
    "censored(time, status) ~ 1 + sex + age", 
    df_kidney, 
    family="gamma",
    link="log"
)

where time is a survival time and status is either "right" or "none" indicating right-censoring and no censoring, repsectively.

---

But there's an additional feature I discuss below

In addition, bmb.Model gains an extra argument called center_predictors. By default, when there's an intercept, Bambi centers all the predictors in the design matrix. This usually results in better sampling speed because (if I'm correct) mitigates some correlation in the posterior.

However, while I was testing the new censored models I was having lots of sampling initialization problems. I think it was due to a combination of the prior choice and the scale of the predictor variable, that when centered, caused the issue. So by doing bmb.Model(..., center_predictors=False) we get rid of the centering and the problem disappears.

Notice the center_predictors=False may also be usefu.l to people that just want to make sure the intercept they select for the prior has the natural interpretation they expect. With automatic centering, the interpretation of the intercept is not what you think it is (unless all your predictors are centered around zero)

[codecov-commenter]

Codecov Report

Merging #697 (3f85e4f) into main (6268ccf) will decrease coverage by 0.66%.
The diff coverage is 87.50%.

@@            Coverage Diff             @@
##             main     #697      +/-   ##
==========================================
- Coverage   89.52%   88.87%   -0.66%     
==========================================
  Files          40       43       +3     
  Lines        2951     3362     +411     
==========================================
+ Hits         2642     2988     +346     
- Misses        309      374      +65     

Impacted Files	Coverage Δ
bambi/data/datasets.py	96.15% <ø> (ø)
bambi/defaults/families.py	87.50% <ø> (ø)
bambi/families/univariate.py	94.38% <77.77%> (-1.74%)	⬇️
bambi/backend/model_components.py	100.00% <100.00%> (ø)
bambi/backend/pymc.py	94.94% <100.00%> (ø)
bambi/backend/terms.py	96.65% <100.00%> (+0.18%)	⬆️
bambi/models.py	79.22% <100.00%> (+0.05%)	⬆️
bambi/terms/response.py	79.36% <100.00%> (+0.67%)	⬆️
bambi/terms/utils.py	93.75% <100.00%> (+93.75%)	⬆️

... and 8 files with indirect coverage changes

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