- formula
A two-sided linear formula object describing both the
fixed-effects and random-effects parts of the longitudinal submodel
similar in vein to formula specification in the **lme4** package
(see `glmer`

or the **lme4** vignette for details).
Note however that the double bar (`||`

) notation is not allowed
when specifying the random-effects parts of the formula, and neither
are nested grouping factors (e.g. `(1 | g1/g2))`

or
`(1 | g1:g2)`

, where `g1`

, `g2`

are grouping factors.
For a multivariate GLM this should be a list of such formula objects,
with each element of the list providing the formula for one of the
GLM submodels.

- data
A data frame containing the variables specified in
`formula`

. For a multivariate GLM, this can
be either a single data frame which contains the data for all
GLM submodels, or it can be a list of data frames where each
element of the list provides the data for one of the GLM submodels.

- family
The family (and possibly also the link function) for the
GLM submodel(s). See `glmer`

for details.
If fitting a multivariate GLM, then this can optionally be a
list of families, in which case each element of the list specifies the
family for one of the GLM submodels. In other words, a different family
can be specified for each GLM submodel.

- weights
Same as in `glm`

,
except that when fitting a multivariate GLM and a list of data frames
is provided in `data`

then a corresponding list of weights
must be provided. If weights are
provided for one of the GLM submodels, then they must be provided for
all GLM submodels.

- prior, prior_intercept, prior_aux
Same as in `stan_glmer`

except that for a multivariate GLM a list of priors can be provided for
any of `prior`

, `prior_intercept`

or `prior_aux`

arguments.
That is, different priors can optionally be specified for each of the GLM
submodels. If a list is not provided, then the same prior distributions are
used for each GLM submodel. Note that the `"product_normal"`

prior is
not allowed for `stan_mvmer`

.

- prior_covariance
Cannot be `NULL`

; see `priors`

for
more information about the prior distributions on covariance matrices.
Note however that the default prior for covariance matrices in
`stan_mvmer`

is slightly different to that in `stan_glmer`

(the details of which are described on the `priors`

page).

- prior_PD
A logical scalar (defaulting to `FALSE`

) indicating
whether to draw from the prior predictive distribution instead of
conditioning on the outcome.

- algorithm
A string (possibly abbreviated) indicating the
estimation approach to use. Can be `"sampling"`

for MCMC (the
default), `"optimizing"`

for optimization, `"meanfield"`

for
variational inference with independent normal distributions, or
`"fullrank"`

for variational inference with a multivariate normal
distribution. See `rstanarm-package`

for more details on the
estimation algorithms. NOTE: not all fitting functions support all four
algorithms.

- adapt_delta
Only relevant if `algorithm="sampling"`

. See
the adapt_delta help page for details.

- max_treedepth
A positive integer specifying the maximum treedepth
for the non-U-turn sampler. See the `control`

argument in
`stan`

.

- init
The method for generating initial values. See
`stan`

.

- QR
A logical scalar defaulting to `FALSE`

, but if `TRUE`

applies a scaled `qr`

decomposition to the design matrix. The
transformation does not change the likelihood of the data but is
recommended for computational reasons when there are multiple predictors.
See the QR-argument documentation page for details on how
rstanarm does the transformation and important information about how
to interpret the prior distributions of the model parameters when using
`QR=TRUE`

.

- sparse
A logical scalar (defaulting to `FALSE`

) indicating
whether to use a sparse representation of the design (X) matrix.
If `TRUE`

, the the design matrix is not centered (since that would
destroy the sparsity) and likewise it is not possible to specify both
`QR = TRUE`

and `sparse = TRUE`

. Depending on how many zeros
there are in the design matrix, setting `sparse = TRUE`

may make
the code run faster and can consume much less RAM.

- ...
Further arguments passed to the function in the rstan
package (`sampling`

,
`vb`

, or
`optimizing`

),
corresponding to the estimation method named by `algorithm`

. For example,
if `algorithm`

is `"sampling"`

it is possible to specify `iter`

,
`chains`

, `cores`

, and other MCMC controls.

Another useful argument that can be passed to rstan via `...`

is
`refresh`

, which specifies how often to print updates when sampling
(i.e., show the progress every `refresh`

iterations). `refresh=0`

turns off the iteration updates.