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Releases: koenderks/jfa

CRAN-v0.3.1

12 Oct 18:38
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jfa 0.3.1

Bug fixes

  • Fixed a bug in the planning() function that did not allow the user to plan for a monetary sample when their population size was too low.
  • Fixed a bug in the planning() function that did not allow the user to select a non-integer number of expected errors when there was a prior involved.

Minor changes

  • Added unit tests that regularly verify results of the planning() and evaluation() functions against benchmarks.

CRAN-v0.3.0

15 Sep 16:04
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jfa 0.3.0

New features

  • Implemented the argument counts in the evaluation() function that quantifies how many times each observation should be evaluated due to being selected multiple times in the selection stage.

CRAN-v0.2.0

06 Aug 11:06
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jfa 0.2.0

New features

  • Implemented prior construction methods none, median, hypotheses, sample, and factor in the auditPrior() function. In addition to the already supported arm method, these methods allow the auditor to incorporate more sources of audit information into the prior distribution.
  • Implemented minPrecision argument in the planning() function that allows auditors to calculate a sample size so that the difference between the posterior upper confidence bound and the most likely error is lower than the set minimum precision. Also implemented in the evaluation() function as a requirement to approve the population.
  • Return the value mle from the evaluation() function, which quantifies the most likely error. Also return the value of the precision from this function.
  • Implemented increase argument in the planning() function that allows the user to increase the sample size with a set amount each step of the iterations.

Minor changes

  • Implemented more efficient versions of the monetary unit sampling algorithms.
  • Changed the x-axis labels in the default plot to theta instead of misstatement.

CRAN v0.0.1

08 Jan 23:33
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jfa 0.1.0

New features

  • First version of the jfa package. The package provides four functions: auditPrior(), planning(), sampling(), and evaluation().