**Minor changes**

- Resolved CRAN note
`Warning: <img> attribute "align" not allowed for HTML5`

. - Improve compatibility with
`R 4.2.0`

.

**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.

**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.

**New features**

- Added argument
`randomize`

to the`selection()`

function, which allows the user to randomly shuffle the items in the population before selection. Note that specifying`randomize = TRUE`

overrules`order`

.

**Bug fixes**

- Fixed a bug where the maximum sample size was not reached (i.e.,
`planning`

stopped at`max`

- 1).

**Major changes**

- The
`order`

argument in`selection()`

no longer accepts a logical, but instead takes the column name of the ranking variable in the`data`

.

**Minor changes**

- Adjusted an error in the text of the selection vignette.
- Rewritten most of the vignettes.

**New features**

- Added argument
`alternative`

with possible options`less`

(default),`two.sided`

, and`greater`

to the`evaluation()`

function that allows control over the type of hypothesis test to perform and the type of confidence / credible interval to calculate. - Added
`predict.jfaPrior()`

and`predict.jfaPosterior()`

that produce predictions for the data under the prior or posterior distribution. - Added
`method = 'param'`

to function`auditPrior()`

which takes as input the raw`alpha`

and`beta`

parameters of the prior distribution. - Added
`method = 'strict'`

to function`auditPrior()`

which constructs an (improper) prior distribution that yields the same results (with respect to sample sizes and upper limits) as classical procedures. - Added the modified seed sampling algorithm
(
`method = 'sieve')`

to`selection()`

. - Added a new vignette that describes the sampling methodology
implemented in
`jfa`

. - objects from
`auditPrior()`

,`planning()`

, and`evaluation()`

now contain information about the posterior predictive distribution when`N.units`

is specified.

**Major changes**

- From
`jfa`

0.5.7 to`jfa`

0.6.0 there has been a major overhaul in the names of function arguments. This is done so that the calls integrate better with general R syntax and the package gets more user-friendly. I apologize for any inconvenience this may cause. The following names have been changed:`median`

->`impartial`

(in`auditPrior()`

)`sampleK`

->`x`

(in`auditPrior()`

)`sampleN`

->`n`

(in`auditPrior()`

)`N`

->`N.units`

(in`auditPrior()`

)`maxSize`

->`max`

(in`planning()`

)`increase`

->`by`

(in`planning()`

)`withReplacement`

->`replace`

(in`selection()`

)`ordered`

->`order`

(in`selection()`

)`ascending`

->`decreasing`

(in`selection()`

)`intervalStartingPoint`

->`start`

(in`selection()`

)`algorithm`

->`method`

(in`selection()`

)`expectedErrors`

->`expected`

(in`auditPrior()`

and`planning()`

)`confidence`

->`conf.level`

(in`auditPrior()`

,`planning()`

, and`evaluation()`

)`pHmin`

->`p.hmin`

(in`auditPrior()`

)`minPrecision`

->`min.precision`

(in`auditPrior()`

,`planning()`

, and`evaluation()`

)`population`

->`data`

(in`selection()`

)`kSumstats`

->`x`

(in`evaluation()`

)`nSumstats`

->`n`

(in`evaluation()`

)`sample`

->`data`

(in`evaluation()`

)`bookValues`

->`values`

(in`selection()`

and`evaluation()`

)`auditValues`

->`values.audit`

(in`evaluation()`

)`counts`

->`times`

(in`evaluation()`

)`popBookValues`

->`N.units`

(in`evaluation()`

)`rohrbachDelta`

->`r.delta`

(in`evaluation()`

)`momentPopType`

->`m.type`

(in`evaluation()`

)`csA`

->`cs.a`

(in`evaluation()`

)`csB`

->`cs.b`

(in`evaluation()`

)`csMu`

->`cs.mu`

(in`evaluation()`

)`records`

->`items`

(in`selection()`

)`mus`

->`values`

(in`selection()`

)`hypotheses`

->`hyp`

(in`auditPrior()`

)

`poisson`

is now the default likelihood / method for all functions since it is the most conservative.`method = 'interval'`

is now the default selection method.- The default prior distributions used when
`method = 'default'`

or`prior = TRUE`

are now set to the`gamma(1, 1)`

,`beta(1,1)`

, and`beta-binomial(1, 1)`

priors. - The
`times`

(former`counts`

) argument in`evaluation()`

must now be indicated as a column name in the`data`

instead of a vector. `nPrior`

and`kPrior`

have been removed from the`planning()`

and`evaluation()`

functions. All prior distributions must now be specified using`prior = TRUE`

(noninformative priors) or using a call to`auditPrior()`

.- Removed the
`auditBF()`

function since its value is available through`evaluation(materiality = x, prior = auditPrior(method = 'impartial', materiality = x))`

**Minor changes**

- It is now allowed for
`x`

and`n`

to have the same value in`evaluation()`

. - The parameters for an impartial beta-binomial prior are now calculated more efficiently in the case of zero expected errors.

**Minor changes**

- The logo is now displayed in the
`?jfa-package`

help file. - The cheat sheet link has changed in the README file.

**Bug fixes**

- Fixed a bug in the
`print.jfaEvaluation()`

call if there was no performance materiality specified and`prior = TRUE`

.

**New features**

- The
`print()`

functions now return a more concise description of the relevant output. - Added
`summary()`

functions for all returned objects that take over the former (elaborate) output of the`print()`

functions. - Implemented a new function
`auditBF()`

which computes Bayes factors from summary statistics of an audit sample.

**Bug fixes**

- Fixed a bug in
`evaluation()`

in which the likelihood stored in the prior was not properly passed to the function. - Fixed an error in the calculation of the posterior mode of the beta distribution.

**Minor changes**

- Restored the default value (0.95) for the ‘confidence’ argument in all applicable functions.

**New features**

- Objects with class
`jfaPosterior`

as returned by`evaluation()$posterior`

and`planning()$expectedPosterior`

can now be used as input for the`prior`

argument in the`planning()`

and`evaluation()`

functions.

**Bug fixes**

- Fixed a bug in
`method = 'bram'`

in the`auditPrior()`

function where the prior parameters would go off to infinity when`expectedError = 0`

.

**Major changes**

- Now calculates the upper bound for the population errors according
to the hypergeometric distribution via an inverted hypothesis test. As a
result of this method, the
`planning()`

function does not require a value for the`materiality`

anymore when planning with the`hypergeometric`

likelihood.

**Minor changes**

- Added a benchmark for the
`MUS`

package to the unit tests. - Improved plots with better titles and axes labels.

**New features**

- Made
`expectedErrors > 0`

available for`method = 'hypotheses'`

in the`auditPrior()`

function. - Made
`method = 'hypotheses'`

and`method = 'impartial'`

in the`auditPrior()`

function available for`likelihood = 'hypergeometric'`

. - Added
`bram`

as a method for the`auditPrior()`

function.`method = 'bram'`

computes a prior distribution with a given mode (`expectedError`

) and upper bound (`ub`

).

**Bug fixes**

- Fixed an error in the mode of the gamma posterior distribution from
the
`evaluation()`

function in which`+1`

was added to the beta parameter, resulting in slightly lower modes than the correct ones. - Made a correction to the calculation of the beta-binomial prior and
posterior so that the posterior parameter
`N`

has the correct value of`N = N - n`

(current) instead of`N - n + k`

(before).

**Major changes**

- Removed the default value
`confidence = 0.95`

in all applicable functions.`confidence`

currently has no default value so that the user is required to give an input. - Changed the default
`likelihood = 'poisson'`

in the`planning()`

function to`likelihood = 'binomial'`

to be consistent across all functions. - Changed the order of most function arguments so that
`materiality`

and`minPrecision`

are among the first ones to be shown.

**Minor changes**

- Updated the documentation for all functions with more simple examples.

**New features**

- Update the poisson evaluation calculation so that it allows for fractional errors.

**Bug fixes**

- Fixed an error in the hypergeometric upper bound calculation that
was accidentally based on the
`phyper()`

function instead of the`qhyper()`

function, which resulted in lower bounds than usual.

**Minor changes**

- Add statistical tables with output (sample sizes, upper limits, Bayes factors) to the GitHub repository in pdf format.
- Changed the computation method of the sample sizes for hypergeometric and beta-binomial distributions so that they are faster.

**Bug fixes**

- Reduced the size of the tarball by adding files to the .Rbuildignore
- Fixed a bug in
`selection()`

where if`population`

is sorted or modified,`bv`

still retained the old ordering and data. The resulting sample was overweighted towards small values and/or still contained negative values (Thanks to @alvanson).

**New features**

- Add a function
`report()`

that automatically generates an audit report.

**Major changes**

- Removed the
`sampling()`

function, which is now replaced entirely with the`selection()`

function. - Changed the output of the
`evaluation()`

function when an estimator is used.

**New features**

- Added
`digits`

argument in the internal`jfa:::print.jfaPrior()`

,`jfa:::print.jfaPlanning()`

,`jfa:::print.jfaSelection()`

, and`jfa:::print.jfaEvaluation()`

functions to control rounding in printing. - Added
`description`

,`statistics`

,`specifics`

and`hypotheses`

to the output of the`auditPrior()`

function. - Added class
`jfaPosterior`

with`print()`

and`plot()`

methods. - Added
`expectedPosterior`

of class`jfaPosterior`

to the output of the`planning()`

function, includes`description`

,`statistics`

and`hypotheses`

. - Added
`posterior`

of class`jfaPosterior`

to the output of the`evaluation()`

function, includes`description`

,`statistics`

and`hypotheses`

.

**Bug fixes**

- Implemented improved calculation of prior parameters in the
`auditPrior()`

function for`method = impartial`

when`expectedErrors > 0`

.

**Major changes**

- Add a warning message to the
`sampling()`

function that it will be deprecated from 0.5.0 onward. You can use`selection()`

instead, since`sampling()`

causes namespace issues with other packages.

**Minor changes**

- Changed the class
`jfaSampling`

to`jfaSelection`

. This should not have any consequences.

**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.

**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.

**New features**

- Implemented prior construction methods
`default`

,`impartial`

,`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.

**New features**

- First version of the
`jfa`

package. The package provides four functions:`auditPrior()`

,`planning()`

,`sampling()`

, and`evaluation()`

.