Helper functions to specify linear and non-linear
formulas for use with `brmsformula`

.

```
nlf(formula, ..., flist = NULL, dpar = NULL, resp = NULL, loop = NULL)
lf(
...,
flist = NULL,
dpar = NULL,
resp = NULL,
center = NULL,
cmc = NULL,
sparse = NULL,
decomp = NULL
)
acformula(autocor, resp = NULL)
set_nl(nl = TRUE, dpar = NULL, resp = NULL)
set_rescor(rescor = TRUE)
set_mecor(mecor = TRUE)
```

- formula
Non-linear formula for a distributional parameter. The name of the distributional parameter can either be specified on the left-hand side of

`formula`

or via argument`dpar`

.- ...
Additional

`formula`

objects to specify predictors of non-linear and distributional parameters. Formulas can either be named directly or contain names on their left-hand side. Alternatively, it is possible to fix parameters to certain values by passing numbers or character strings in which case arguments have to be named to provide the parameter names. See 'Details' for more information.- flist
Optional list of formulas, which are treated in the same way as formulas passed via the

`...`

argument.- dpar
Optional character string specifying the distributional parameter to which the formulas passed via

`...`

and`flist`

belong.- resp
Optional character string specifying the response variable to which the formulas passed via

`...`

and`flist`

belong. Only relevant in multivariate models.- loop
Logical; Only used in non-linear models. Indicates if the computation of the non-linear formula should be done inside (

`TRUE`

) or outside (`FALSE`

) a loop over observations. Defaults to`TRUE`

.- center
Logical; Indicates if the population-level design matrix should be centered, which usually increases sampling efficiency. See the 'Details' section for more information. Defaults to

`TRUE`

for distributional parameters and to`FALSE`

for non-linear parameters.- cmc
Logical; Indicates whether automatic cell-mean coding should be enabled when removing the intercept by adding

`0`

to the right-hand of model formulas. Defaults to`TRUE`

to mirror the behavior of standard R formula parsing.- sparse
Logical; indicates whether the population-level design matrices should be treated as sparse (defaults to

`FALSE`

). For design matrices with many zeros, this can considerably reduce required memory. Sampling speed is currently not improved or even slightly decreased.- decomp
Optional name of the decomposition used for the population-level design matrix. Defaults to

`NULL`

that is no decomposition. Other options currently available are`"QR"`

for the QR decomposition that helps in fitting models with highly correlated predictors.- autocor
A one sided formula containing autocorrelation terms. All none autocorrelation terms in

`autocor`

will be silently ignored.- nl
Logical; Indicates whether

`formula`

should be treated as specifying a non-linear model. By default,`formula`

is treated as an ordinary linear model formula.- rescor
Logical; Indicates if residual correlation between the response variables should be modeled. Currently this is only possible in multivariate

`gaussian`

and`student`

models. Only relevant in multivariate models.- mecor
Logical; Indicates if correlations between latent variables defined by

`me`

terms should be modeled. Defaults to`TRUE`

.

For `lf`

and `nlf`

a `list`

that can be
passed to `brmsformula`

or added
to an existing `brmsformula`

or `mvbrmsformula`

object.
For `set_nl`

and `set_rescor`

a logical value that can be
added to an existing `brmsformula`

or `mvbrmsformula`

object.

```
# add more formulas to the model
bf(y ~ 1) +
nlf(sigma ~ a * exp(b * x)) +
lf(a ~ x, b ~ z + (1|g)) +
gaussian()
#> y ~ 1
#> sigma ~ a * exp(b * x)
#> a ~ x
#> b ~ z + (1 | g)
# specify 'nl' later on
bf(y ~ a * inv_logit(x * b)) +
lf(a + b ~ z) +
set_nl(TRUE)
#> y ~ a * inv_logit(x * b)
#> a ~ z
#> b ~ z
# specify a multivariate model
bf(y1 ~ x + (1|g)) +
bf(y2 ~ z) +
set_rescor(TRUE)
#> y1 ~ x + (1 | g)
#> y2 ~ z
# add autocorrelation terms
bf(y ~ x) + acformula(~ arma(p = 1, q = 1) + car(W))
#> y ~ x
#> autocor ~ arma(p = 1, q = 1) + car(W)
```