jfa 0.7.3

New features

• @federicapicogna added fairness_selection() to aid in the decision between fairness metrics. The function has an accompanying print() and plot() method.
• @lottemensink added plot(x, type = "sequential") to the evaluation() function when used with prior, materiality and data. The plot shows Bayes factors as a function of the sample size.

Bug fixes

• Fixed a bug in the evaluation() function using method = "hypergeometric" where the user could provide broken taints. These taints are now rounded to the nearest integer value.

jfa 0.7.2

Minor changes

• In the auditPrior() function, method = "factor" has been replaced with method = "power" to be consistent with existing terminology.
• selection() now always performs sampling with replacement when using items = "values" and method = "random" (Thanks to @lottemensink for the suggestion). This should not lead to substantial differences in typical audit populations.

jfa 0.7.1

New features

• Added the option to calculate a frequentist multi-stage sampling plan with the planning() function. This can be done by providing a vector of integers to the expected argument that specifies the intended sampling plan. For instance, expected = c(1, 0) gives the sample size for a two-stage sampling plan in which the auditor is allowed to extend the initial sample after finding one misstatement and is allowed to approve the population if the initial sample contains no misstatements.
• Added inflated methods to evaluate taintings in the evaluation function via method = "inflated.poisson" and method = "hurdle.beta". Since these methods rely on mcmc sampling, they can take any form of prior. They can also be fitted using maximum likelihood estimation with prior = FALSE.

Minor changes

• For all plotting functions, type = "estimates" is now the default option.

Major changes

• Removed the report() function as it did not align with the rest of the functionality in the package.

Bug fixes

• Fixed a bug in selection() where the numeric data in the sample was returned as character if the original data was entered as character only (i.e., selection(data.frame(x = c("hello", "bye", "hi")), size = 2)).
• N.units is now only taken from the prior if the user does not provide it manually to the planning() and evaluation() functions.

jfa 0.7.0

New features

• Added a new function model_fairness() to compute fairness metrics for algorithm auditing on the basis of predictions of an algorithm.

Minor changes

• In the evaluation() function, the typical stringer bound has been changed from method = "stringer" to method = "stringer.binomial". Furthermore, additional methods stringer.poisson and stringer.hypergeometric are added that apply the stringer bound using the Poisson and hypergeometric distributions, respectively. The stringer method will remain for now but redirect to stringer.binomial.
• Compatibility with rstantools 2.3.1.

jfa 0.6.7

New features

• digit_test() function now returns element estimates containing observed proportions and their corresponding confidence intervals. The plot output also shows these intervals around the estimates.

jfa 0.6.6

Bug fixes

• Changed the links to the images in the vignettes from absolute links to GitHub to relative links as to avoid problems with GitHub servers being unavailable in the future.

jfa 0.6.5

New features

• Additional possibilities to create, plan and evaluate using nonparametric prior distributions.
• Added functionality for stratified evaluation in the evaluation() function.
• Integrated the auditing functionality of the digitTests package via the digit_test() and repeated_test() functions.

Bug fixes

• Fixed a bug where the proportion of value in summary(selection) was displayed incorrectly.

Minor changes

• Improved the error handling and error messages.

jfa 0.6.4

Minor changes

• Resolved CRAN note Warning: <img> attribute "align" not allowed for HTML5.
• Improve compatibility with R 4.2.0.

jfa 0.6.3

Minor changes

• The value for x in evaluation() and expected in planning() is now automatically ceiled (while throwing a warning) for method = "hypergeometric".
• Improved overall documentation.

jfa 0.6.2

Major changes

• Removed the output and functions related to the class jfaPredictive. The probabilities for the prior and posterior predictive distributions can be obtained by calling the predict() function.

Minor changes

• The value for N.units in auditPrior(), planning(), and evaluation() is now automatically ceiled for likelihood/method = 'hypergeometric'.
• Implemented a warning message when the likelihood in the prior does not match with the likelihood/method inputs for planning() and evaluation(). The likelihood from the prior is leading in these cases and will overrule the other options.