Changes in version 3.0.0 (2026-05-28) Major Changes - Functionality to create perturbed table using BigQuery including validation process to be able to work with large datasets in a Google Cloud Platform environment. - Generate record keys from ons_id as default where ons_id exists, with an option to turn it off. Minor improvements and bug fixes - Created functions to generate sample microdata, ptable_10_5, and random record keys for testing purposes, with alternative parameters allowed. The sample microdata included in this package is identical to the sample in the python version (cell-key-perturbation). However, a new sample of microdata created in R with the same parameters would not be identical to the sample in python, due to differences in Random Number Generation. - Validation moved to a separate module with improved and additional validation checks. - Tabulation with missing values: Missing values will be included in the frequency table, treating missingness as a category. Added a check function for missing values in tabulation variables, which returns a warning message if any of the tabulation variables contains missing values and suggests to consider removing them. - Re-ordered columns and standardised the data type for output tables. Changes in version 2.0.0 - New threshold parameter added (with default of 10) for the create_perturbed_table function. Counts < threshold will be set to missing. This means that the user will not need to go through an additional process to suppress small counts after applying perturbation. - Warnings if any records are missing record keys and exception raised if percentage with record keys < 50%. - The 'record_key_arg' parameter renamed to 'record_key' to match the Python implementation. Changes in version 1.0.0 - First release Changes in version 0.0.0.9000