CRAN-v0.6.0

jfa 0.6.0

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.