Persistent Data Anonymization Pipeline

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Documentation for package ‘deident’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

adaptive_noise	Function factory to apply white noise to a vector proportional to the spread of the data
add_blur	De-identification via categorical aggregation
add_encrypt	De-identification via hash encryption
add_group	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
add_ungroup	Add aggregation to pipelines
apply_deident	Apply a 'deident' pipeline
apply_to_data_frame	Apply a 'deident' pipeline to a new data frame
BaseDeident	Base class for all De-identifier classes
Blurer	Deidentifier class for applying 'blur' transform
category_blur	Utility for producing 'blur'
create_deident	Create a deident pipeline
deident	Define a transformation pipeline
deident_job_from_folder	Apply a pipeline to files on disk.
Drop	R6 class for the removal of variables from a pipeline
Encrypter	Deidentifier class for applying 'encryption' transform
from_yaml	Restore a serialized deident from file
GroupedShuffler	GroupedShuffler class for applying 'shuffling' transform with data aggregated
lognorm_noise	Function factory to apply log-normal noise to a vector
NumericBlurer	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
starwars	Starwars characters
white_noise	Function factory to apply white noise to a vector