Functions required for compatibility of brms with emmeans.
Users are not required to call these functions themselves. Instead,
they will be called automatically by the `emmeans`

function
of the emmeans package.

```
recover_data.brmsfit(
object,
data,
resp = NULL,
dpar = NULL,
nlpar = NULL,
re_formula = NA,
epred = FALSE,
...
)
emm_basis.brmsfit(
object,
trms,
xlev,
grid,
vcov.,
resp = NULL,
dpar = NULL,
nlpar = NULL,
re_formula = NA,
epred = FALSE,
...
)
```

- object
An object of class

`brmsfit`

.- data, trms, xlev, grid, vcov.
Arguments required by emmeans.

- resp
Optional names of response variables. If specified, predictions are performed only for the specified response variables.

- dpar
Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned.

- nlpar
Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned.

- re_formula
Optional formula containing group-level effects to be considered in the prediction. If

`NULL`

, include all group-level effects; if`NA`

(default), include no group-level effects.- epred
Logical. If

`TRUE`

compute predictions of the posterior predictive distribution's mean (see`posterior_epred.brmsfit`

) while ignoring arguments`dpar`

and`nlpar`

. Defaults to`FALSE`

. If you have specified a response transformation within the formula, you need to set`epred`

to`TRUE`

for emmeans to detect this transformation.- ...
Additional arguments passed to emmeans.

In order to ensure compatibility of most brms models with
emmeans, predictions are not generated 'manually' via a design matrix
and coefficient vector, but rather via `posterior_linpred.brmsfit`

.
This appears to generally work well, but note that it produces an `.@linfct`
slot that contains the computed predictions as columns instead of the
coefficients.

```
if (FALSE) {
fit1 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
data = kidney, family = lognormal())
summary(fit1)
# summarize via 'emmeans'
library(emmeans)
rg <- ref_grid(fit1)
em <- emmeans(rg, "disease")
summary(em, point.est = mean)
# obtain estimates for the posterior predictive distribution's mean
epred <- emmeans(fit1, "disease", epred = TRUE)
summary(epred, point.est = mean)
# model with transformed response variable
fit2 <- brm(log(mpg) ~ factor(cyl), data = mtcars)
summary(fit2)
# results will be on the log scale by default
emmeans(fit2, ~ cyl)
# log transform is detected and can be adjusted automatically
emmeans(fit2, ~ cyl, epred = TRUE, type = "response")
}
```