Changes in version 1.2.0                        

  - New function duplicated_id_check() checks for duplicated subject IDs
    in the data set (returns a warning, as this is allowed in
    longitudinal studies)
  - New function ascii_check() scans both the data dictionary and data
    set for (1) non-ASCII characters (e.g., é, ñ) and (2) newline (\n)
    and carriage return (\r) characters
  - New helper ascii_cleaner() cleans a data frame by (1) converting
    smart quotes to straight quotes, replacing accented characters with
    ASCII equivalents, removing newline and carriage return characters
  - complete_check(): updated to include both duplicated_id_check() and
    ascii_check()
  - values_check(): updated Check 1 to require each VALUES cell contain
    exactly one equals sign (=) (e.g., 1=Yes vs. 1=Yes; 0=No), in
    alignment with dbGaP formatting requirements; added new Check 5 to
    detect duplicated MEANINGs in VALUES=MEANING entries.
  - misc_format_check(): now prevents errors when the VALUES column is
    the last column in the data dictionary (i.e., no columns follow) -
    this does return a WARNING, however, as this structure is valid but
    expected to be uncommon
  - integer_check(): resolved a rare error when the TYPE column contains
    malformed or unexpected values
  - Documentation: Updated to emphasize the importance of reading CSVs
    using readr::read_csv(..., na = c("", "NA")) or read.csv(...,
    na.strings = c("", "NA")) to correctly interpret missing cells,
    particularly in the VALUES column. (See GitHub Issue #16 for
    discussion)

                        Changes in version 1.1.1                        

  - minmax_check: adjusted to return a sorted list of out of range
    values and polished documentation to be more informative
  - values_check: corrected bug in code that was not detecting
    leading/trailing zeros in VALUES columns
  - name_correct: when a user runs name_correct when it is not needed a
    new message informing them of no discrepancies detected will print

                 Changes in version 1.1.0 (2023-09-27)                  

  - added an informative error message when the required VALUES column
    is missing
  - adjusted values_check to temporarily create dummy names for
    blank-named columns beyond VALUES to prevent function from dying if
    column names after VALUES are blank strings
  - corrected minmax_check and integer_check bugs that occurred when
    SUBJECT_ID was a character vector
  - adjusted misc_format_check to return a WARNING that alerts users if
    they read in a data set and R automatically fills in column names
    after VALUES (which is allowed by the package, but not dbGaP itself)
  - adjusted NA_check to correctly capture NA=N/A VALUES
  - corrected bug in type_check that was allowing some non-allowable
    TYPE entries to pass
  - corrected but in missing_value_check that was flagging some
    variables even when they had properly encoded NA=N/A VALUES
  - made complete_check more robust to errors by wrapping functions in
    tryCatch
  - used seealso to link utility functions to relevant check functions

                 Changes in version 1.0.2 (2023-02-22)                  

  - removed row numbers from data set files
  - renamed data dictionary files by removing "SSM" acronym (done to
    avoid confusion as this means “subject sample mapping” and is
    intended for use with other dbGaP data files)
  - updated id_check() to include a check for missing SUBJECT_IDs (not
    allowed by dbGaP)
  - updated row_check() to check for duplicate and empty rows in the
    data dictionary (and not just the data set)
  - updated misc_format_check() to check that there are no missing
    VARNAME cells

                 Changes in version 1.0.1 (2022-12-22)                  

  - apply na_if() to one column at a time (vs. entire data frame at
    once) to maintain compatibility with next version of dplyr

                 Changes in version 1.0.0 (2022-11-14)                  

NEWS.md setup

  - added NEWS.md