jfa (version 0.7.3)

jfa-package: jfa --- Statistical Methods for Auditing

Description

jfa is an R package that provides statistical methods for auditing. The package includes functions for planning, performing, and evaluating an audit sample compliant with international auditing standards, as well as functions for auditing data, such as testing the distribution of leading digits in the data against Benford's law. In addition to offering classical frequentist methods, jfa also provides a straightforward implementation of their Bayesian counterparts.

The functionality of the jfa package and its intended workflow are implemented with a graphical user interface in the Audit module of JASP, a free and open-source software program for statistical analyses.

For documentation on jfa itself, including the manual and user guide for the package, worked examples, and other tutorial information visit the package website.

Author

Koen Derks (maintainer, author)

<[e-mail]>

Please use the citation provided by R when citing this package. A BibTex entry is available from citation('jfa').

See Also

Useful links:

• The vignettes for worked examples.

• The issue page to submit a bug report or feature request.

Examples

# Load the jfa package
library(jfa)

#################################
### Example 1: Audit sampling ###
#################################

# Load the BuildIt population
data('BuildIt')

# Stage 1: Planning
stage1 <- planning(materiality = 0.03, expected = 0.01)
summary(stage1)

# Stage 2: Selection
stage2 <- selection(data = BuildIt, size = stage1,
                    units = 'values', values = 'bookValue',
                    method = 'interval', start = 1)
summary(stage2)

# Stage 3: Execution
sample <- stage2[['sample']]

# Stage 4: Evaluation
stage4 <- evaluation(data = sample, method = 'stringer.binomial',
                     values = 'bookValue', values.audit = 'auditValue')
summary(stage4)

#################################
### Example 2: Data auditing ####
#################################

# Load the sinoForest data set
data('sinoForest')

# Test first digits in the data against Benford's law
digit_test(sinoForest[["value"]], check = "first", reference = "benford")

######################################
### Example 3: Algorithm auditing ####
######################################

# Load the compas data set
data('compas')

# Test algorithmic fairness against Caucasian ethnicity
model_fairness(compas, "Ethnicity", "TwoYrRecidivism", "Predicted",
               privileged = "Caucasian", positive = "yes")