Reference

Deident
deident()	Define a transformation pipeline
create_deident()	Create a deident pipeline
apply_deident()	Apply a 'deident' pipeline
apply_to_data_frame()	Apply a 'deident' pipeline to a new data frame
deident_job_from_folder()	Apply a pipeline to files on disk.
API Functional interface to the deident methods.
add_blur()	De-identification via categorical aggregation
add_encrypt()	De-identification via hash encryption
add_group() add_ungroup()	Add aggregation to pipelines
add_numeric_blur()	De-identification via numeric aggregation
add_perturb()	De-identification via random noise
add_pseudonymize()	De-identification via replacement
add_shuffle()	De-identification via random sampling
Dataset
ShiftsWorked	Synthetic data set listing daily shift pattern for fictitious employees
starwars	Starwars characters
Utilities
category_blur()	Utility for producing 'blur'
adaptive_noise()	Function factory to apply white noise to a vector proportional to the spread of the data
lognorm_noise()	Function factory to apply log-normal noise to a vector
white_noise()	Function factory to apply white noise to a vector
from_yaml()	Restore a serialized deident from file
R6 Classes
BaseDeident	Base class for all De-identifier classes
Blurrer	Deidentifier class for applying 'blur' transform
Drop	R6 class for the removal of variables from a pipeline
Encrypter	Deidentifier class for applying 'encryption' transform
GroupedShuffler	GroupedShuffler class for applying 'shuffling' transform with data aggregated
NumericBlurrer	Group numeric data into baskets
Perturber	R6 class for deidentification via random noise
Pseudonymizer	R6 class for deidentification via replacement
ShiftsWorked	Synthetic data set listing daily shift pattern for fictitious employees
Shuffler	Shuffler class for applying 'shuffling' transform