NEWS.md
Bug fixes
New features
evaluation()
function.digitTests
package via the digit_test()
and repeated_test()
functions.Bug fixes
summary(selection)
was displayed incorrectly.Minor changes
Minor changes
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.R 4.2.0
.Minor changes
x
in evaluation()
and expected
in planning()
is now automatically ceiled (while throwing a warning) for method = "hypergeometric"
.Major changes
jfaPredictive
. The probabilities for the prior and posterior predictive distributions can be obtained by calling the predict()
function.Minor changes
N.units
in auditPrior()
, planning()
, and evaluation()
is now automatically ceiled for likelihood
/method
= 'hypergeometric'
.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
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
planning
stopped at max
- 1).Major changes
order
argument in selection()
no longer accepts a logical, but instead takes the column name of the ranking variable in the data
.Minor changes
New features
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.predict.jfaPrior()
and predict.jfaPosterior()
that produce predictions for the data under the prior or posterior distribution.method = 'param'
to function auditPrior()
which takes as input the raw alpha
and beta
parameters of the prior distribution.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.method = 'sieve')
to selection()
.jfa
.auditPrior()
, planning()
, and evaluation()
now contain information about the posterior predictive distribution when N.units
is specified.Major changes
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.method = 'default'
or prior = TRUE
are now set to the gamma(1, 1)
, beta(1,1)
, and beta-binomial(1, 1)
priors.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()
.auditBF()
function since its value is available through evaluation(materiality = x, prior = auditPrior(method = 'impartial', materiality = x))
Minor changes
x
and n
to have the same value in evaluation()
.Minor changes
?jfa-package
help file.Bug fixes
print.jfaEvaluation()
call if there was no performance materiality specified and prior = TRUE
.New features
print()
functions now return a more concise description of the relevant output.summary()
functions for all returned objects that take over the former (elaborate) output of the print()
functions.auditBF()
which computes Bayes factors from summary statistics of an audit sample.Bug fixes
evaluation()
in which the likelihood stored in the prior was not properly passed to the function.Minor changes
New features
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
method = 'bram'
in the auditPrior()
function where the prior parameters would go off to infinity when expectedError = 0
.Major changes
planning()
function does not require a value for the materiality
anymore when planning with the hypergeometric
likelihood.Minor changes
MUS
package to the unit tests.New features
expectedErrors > 0
available for method = 'hypotheses'
in the auditPrior()
function.method = 'hypotheses'
and method = 'impartial'
in the auditPrior()
function available for likelihood = 'hypergeometric'
.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
evaluation()
function in which +1
was added to the beta parameter, resulting in slightly lower modes than the correct ones.N
has the correct value of N = N - n
(current) instead of N - n + k
(before).Major changes
confidence = 0.95
in all applicable functions. confidence
currently has no default value so that the user is required to give an input.likelihood = 'poisson'
in the planning()
function to likelihood = 'binomial'
to be consistent across all functions.materiality
and minPrecision
are among the first ones to be shown.Minor changes
New features
Bug fixes
phyper()
function instead of the qhyper()
function, which resulted in lower bounds than usual.Minor changes
Bug fixes
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
report()
that automatically generates an audit report.Major changes
sampling()
function, which is now replaced entirely with the selection()
function.evaluation()
function when an estimator is used.New features
digits
argument in the internal jfa:::print.jfaPrior()
, jfa:::print.jfaPlanning()
, jfa:::print.jfaSelection()
, and jfa:::print.jfaEvaluation()
functions to control rounding in printing.description
, statistics
, specifics
and hypotheses
to the output of the auditPrior()
function.jfaPosterior
with print()
and plot()
methods.expectedPosterior
of class jfaPosterior
to the output of the planning()
function, includes description
, statistics
and hypotheses
.posterior
of class jfaPosterior
to the output of the evaluation()
function, includes description
, statistics
and hypotheses
.Bug fixes
auditPrior()
function for method = impartial
when expectedErrors > 0
.Major changes
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
jfaSampling
to jfaSelection
. This should not have any consequences.Bug fixes
planning()
function that did not allow the user to plan for a monetary sample when their population size was too low.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
planning()
and evaluation()
functions against benchmarks.New features
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
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.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.mle
from the evaluation()
function, which quantifies the most likely error. Also return the value of the precision
from this function.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
New features
jfa
package. The package provides four functions: auditPrior()
, planning()
, sampling()
, and evaluation()
.