This function, given a stacked data frame containing both sample and population data, assesses the generalizability of the sample to the population on given covariates.

## Usage

```
assess(
data,
guided = TRUE,
sample_indicator,
covariates,
estimation_method = "lr",
disjoint_data = TRUE,
trim_pop = FALSE
)
```

## Arguments

- data
data frame comprised of "stacked" sample and target population data

- guided
logical. Default is TRUE. If FALSE, then user must enter all arguments in function to bypass guided mode

- sample_indicator
variable name denoting sample membership (1 = in sample, 0 = out of sample)

- covariates
vector of covariate names in data set that predict sample membership

- estimation_method
method to estimate the probability of sample membership (propensity scores). Default is logistic regression ("lr"). Other methods supported are Random Forests ("rf") and Lasso ("lasso")

- disjoint_data
logical. If TRUE, then sample and population data are considered disjoint. This affects calculation of the weights - see details for more information.

- trim_pop
logical. If TRUE, then population data are subset to exclude individuals with covariates outside bounds of sample covariates.