assign_model: Interpret the model specified by user
In oslerinhealth/baker: Bayesian Analysis Kit for Etiology Research

assign_model translates options specified by a user (e.g., in model_options) into information that can be understood by baker.

1	assign_model(model_options, data_nplcm, silent = TRUE)

`model_options`	See `nplcm()` function.
`data_nplcm`	Data. See `nplcm()` function for data structure.
`silent`	Default is `TRUE` for no messages; `FALSE` otherwise.

assign_model will be modified to check if data are conformable to specified model.

A list of model specifications:

num_slice A vector counting the No. of measurement slices for each level of measurement quality (e.g., MBS, MSS, MGS representing Bronze-Standard Measurements - case-control, Silver-Standard Measurements and Gold-Standard Measurements - case-only);
nested Local dependence specification for modeling bronze-standard data. TRUE for nested models (conditional dependence given disease class); FALSE for non-nested models (conditional independence given disease class). One for each BrS slice.
regression
- do_reg_Eti TRUE for doing etiology regression. It means let the etiology fractions vary with explanatory variables. FALSE otherwise;
- do_reg_FPR A vector whose names represent the slices of bronze-standard data. For each slice of BrS measurements, TRUE does false positive rate regression. It means the false positive rates, estimatable from controls, can vary with covariates; FALSE otherwise.
- is_discrete_predictor A list of names "Eti", and the names for every slice of bronze-standard data. TRUE if all predictors are discrete; FALSE otherwise.