Run automated quality checks on a NONMEM dataset — check_nm

Performs a comprehensive set of checks on a NONMEM-style dataset and returns a summary table with the result of each check. No errors are thrown; all issues are reported as rows in the output table.

Usage

check_nm_dataset(
  data,
  dictionary = NULL,
  exclusion_flag = NULL,
  max_digits = 10,
  max_id_digits = 5,
  extra_reserved = NULL,
  verbose = TRUE
)

Arguments

data: a data.frame (or tibble) containing the NONMEM dataset to check.
dictionary: an optional named list that maps standard NONMEM column names to the actual column names in data. For example, list(ID = "NMID", TIME = "TAD") means the column called "NMID" in data plays the role of ID, and "TAD" plays the role of TIME. Any standard name not listed in the dictionary is assumed to match the column name in data as-is. Supported keys: ID, TIME, DV, MDV, EVID, AMT, CMT, RATE, SS, ADDL, II.
exclusion_flag: character name of the column used as an exclusion flag (for $DATA ACCEPT / IGNORE in NONMEM). If NULL (default), the exclusion-flag check is skipped.
max_digits: integer; threshold for flagging numbers with too many significant digits (default 10).
max_id_digits: integer; preferred maximum number of characters for the ID variable (default 5).
extra_reserved: character vector of additional reserved NONMEM names to check for (e.g. PK parameters used in ADVAN/TRANS such as "CL", "V", "V1", "K12").
verbose: logical; if TRUE (default), messages are printed for every check as it is evaluated.

Value

A data.frame with columns check (short test name), description (human-readable explanation), and result ("PASS", "FAIL", or "NA" when a check is not applicable).