```{r getting-started}
library(mmrm)
fit <- mmrm(
  formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
```

The code specifies an MMRM with the given covariates and an unstructured covariance
matrix for the timepoints (also called visits in the clinical trial context, here
given by `AVISIT`) within the subjects (here `USUBJID`). While by default this uses
restricted maximum likelihood (REML), it is also possible to use ML, see `?mmrm`.

Printing the object will show you output which should be familiar to anyone who
has used any popular modeling functions such as `stats::lm()`, `stats::glm()`,
`glmmTMB::glmmTMB()`, and `lme4::nlmer()`. From this print out we see the function call,
the data used, the covariance structure with number of variance parameters, as well
as the likelihood method, and model deviance achieved. Additionally the user is
provided a printout of the estimated coefficients and the model convergence information:

```{r print}
fit
```

The `summary()` method then provides the coefficients table with Satterthwaite
degrees of freedom as well as the covariance matrix estimate:

```{r summary}
summary(fit)
```
