Prepares the list of data required for geostan's (spatial) measurement error models. Given a data frame of standard errors and any optional arguments, the function returns a list with all required data for the models, filling in missing elements with default values.

- se
Data frame of standard errors; column names must match (exactly) the variable names used in the model formula.

- bounds
An optional numeric vector of length two providing the upper and lower bounds, respectively, of the variables. If not provided, they will be set to c(-Inf, Inf) (i.e., unbounded). Common usages include keeping percentages between zero and one hundred or proportions between zero and one.

- car_parts
A list of data required for spatial CAR models, as created by

`prep_car_data`

; optional. If omitted, the measurement error model will be a non-spatial Student's t model.- prior
A named list of prior distributions (see

`priors`

). If none are provided, default priors will be assigned. The list of priors may include the following parameters:- df
If using a non-spatial ME model, the degrees of freedom (df) for the Student's t model is assigned a gamma prior with default parameters of

`gamma(alpha = 3, beta = 0.2)`

. Provide values for each covariate in`se`

, listing the values in the same order as the columns of`se`

.- location
The prior for the location parameter (mu) is a normal (Gaussian) distribution (the default being

`normal(location = 0, scale = 100)`

). To adjust the prior distributions, provide values for each covariate in`se`

, listing the values in the same order as the columns of se.- scale
The prior for the scale parameters is Student's t, and the default parameters are

`student_t(df = 10, location = 0, scale = 40)`

. To adjust, provide values for each covariate in`se`

, listing the values in the same order as the columns of se.- car_rho
The CAR model, if used, has a spatial autocorrelation parameter,

`rho`

, which is assigned a uniform prior distribution. You must specify values that are within the permissible range of values for`rho`

; these are automatically printed to the console by the`prep_car_data`

function.

- logit
Optional vector of logical values (

`TRUE`

,`FALSE`

) indicating if the variable should be logit-transformed before being modeled. When`TRUE`

, the sampling error will be modeled on the untransformed scale as usual; however, the spatial CAR prior model (or non-spatial Student's t prior model) will be assigned to the logit-transformed variate. Transformation can be crucial for modeling proportions with frequency distributions that are highly skewed.

A list of data as required for (spatial) ME models. Missing arguments will be filled in with default values, including prior distributions.

```
data(georgia)
## for a non-spatial prior model for two covariates
se <- data.frame(ICE = georgia$ICE.se,
college = georgia$college.se)
ME <- prep_me_data(se)
## see default priors
print(ME$prior)
## set prior for the scale parameters
ME <- prep_me_data(se,
prior = list(scale = student_t(df = c(10, 10),
location = c(0, 0),
scale = c(20, 20))))
## for a spatial prior model (often recommended)
A <- shape2mat(georgia, "B")
cars <- prep_car_data(A)
ME <- prep_me_data(se,
car_parts = cars)
```