Update for jfa 0.5.3

koenderks · May 1, 2021 · 6218bf0 · 6218bf0
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: jfa
 Title: Bayesian and Classical Audit Sampling
 Version: 0.5.3
-Date: 2021-04-26
+Date: 2021-05-01
 Authors@R: 
     person(given = "Koen",
            family = "Derks",

diff --git a/NEWS.md b/NEWS.md
@@ -1,14 +1,14 @@
 # jfa 0.5.3
 
-- Changed the default `likelihood = 'poisson'` in the `planning()` function to `likelihood = 'binomial'` to be consistent across functions.
-- Changed the order of most function arguments so that `materiality` and `minPrecision` are among the first ones.
-- Removed the default value of `confidence = 0.95` in all applicable functions, it currently has no default so that the user is required to give an input.
-- Made `method = 'hypotheses'` and `method = 'median'` in the `auditPrior()` function available for `likelihood = 'hypergeometric'`.
+- Updated the documentation for all functions with more simple examples.
+- 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.
 - Made `expectedErrors > 0` available for `method = 'hypotheses'` in the `auditPrior()`.
+- Made `method = 'hypotheses'` and `method = 'median'` 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`).
 - 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 slighly 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).
-- Updated the documentation for the functions with simpler examples.
 
 # jfa 0.5.2
 

diff --git a/R/auditPrior.R b/R/auditPrior.R
@@ -2,7 +2,7 @@
 #'
 #' @description This function creates a prior distribution with audit information to be used in the \code{planning()} and \code{evaluation()} functions via their \code{prior} argument. The function returns an object of class \code{jfaPrior} which can be used with associated \code{print()} and \code{plot()} methods.
 #'
-#' For more details on how to use this function see the package vignette:
+#' For more details on how to use this function, see the package vignette:
 #' \code{vignette('jfa', package = 'jfa')}
 #'
 #' @usage auditPrior(confidence, materiality = NULL, expectedError = 0, 

diff --git a/R/evaluation.R b/R/evaluation.R
@@ -2,7 +2,7 @@
 #'
 #' @description This function takes a data frame (using \code{sample}, \code{bookValues}, and \code{auditValues}) or summary statistics (using \code{nSumstats} and \code{kSumstats}) and performs inference on the misstatement in the sample. The function returns an object of class \code{jfaEvaluation} which can be used with associated \code{print()} and \code{plot()} methods.
 #'
-#' For more details on how to use this function see the package vignette:
+#' For more details on how to use this function, see the package vignette:
 #' \code{vignette('jfa', package = 'jfa')}
 #'
 #' @usage evaluation(confidence, materiality = NULL, minPrecision = NULL, method = 'binomial',

diff --git a/R/planning.R b/R/planning.R
@@ -2,7 +2,7 @@
 #'
 #' @description This function calculates the minimum sample size for a statistical audit sample based on the Poisson, binomial or hypergeometric likelihood. The function returns an object of class \code{jfaPlanning} which can be used with associated \code{print()} and \code{plot()} methods.
 #'
-#' For more details on how to use this function see the package vignette:
+#' For more details on how to use this function, see the package vignette:
 #' \code{vignette('jfa', package = 'jfa')}
 #'
 #' @usage planning(confidence, materiality = NULL, minPrecision = NULL,

diff --git a/R/report.R b/R/report.R
@@ -2,7 +2,7 @@
 #'
 #' @description This function takes an object of class \code{jfaEvaluation} as returned by the \code{evaluation()} function automatically generates a \code{html} or \code{pdf} report containing the analysis results and their interpretation.
 #'
-#' For more details on how to use this function see the package vignette:
+#' For more details on how to use this function, see the package vignette:
 #' \code{vignette('jfa', package = 'jfa')}
 #'
 #' @usage report(object, file = 'report.html', format = 'html_document')

diff --git a/R/selection.R b/R/selection.R
@@ -2,7 +2,7 @@
 #'
 #' @description This function takes a data frame and performs statistical sampling according to one of three algorithms: random sampling, cell sampling, and fixed interval sampling. Sampling is done on the level of two possible sampling units: records or monetary units. The function returns an object of class \code{jfaSelection} which can be used with associated \code{print()} and a \code{plot()} methods.
 #'
-#' For more details on how to use this function see the package vignette:
+#' For more details on how to use this function, see the package vignette:
 #' \code{vignette('jfa', package = 'jfa')}
 #'
 #' @usage selection(population, sampleSize, units = 'records', algorithm = 'random',

diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@
 
 <img src='https://github.com/koenderks/jfa/raw/master/man/figures/readme/logo/jfaLogo.png' width='149' height='173' alt='logo' align='right' margin-left='20' margin-right='20'/>
 
-`jfa` is an R package for statistical audit sampling. The package provides five functions for planning, performing, evaluating, and reporting an audit sample. Specifically, these functions implement standard audit sampling techniques for calculating sample sizes, selecting items from a population, and evaluating the misstatement from a data sample or summary statistics. Additionally, the `jfa` package allows the user to create a prior probability distribution to perform Bayesian audit sampling using these functions.
+`jfa` is an R package for statistical audit sampling. The package provides five functions for planning, performing, evaluating, and reporting an audit sample. Specifically, these functions implement standard audit sampling techniques for calculating sample sizes, selecting items from a population, and evaluating the misstatement from a data sample or from summary statistics. Additionally, the `jfa` package allows the user to create a prior probability distribution to perform Bayesian audit sampling using these functions.
 
 ## Overview
 
@@ -26,7 +26,7 @@ For complete documentation of `jfa`, visit the [package website](https://koender
 
 ## 1. Installation
 
-The released version of `jfa` can be downloaded from [CRAN](https://cran.r-project.org/package=jfa) by running the following command in R or RStudio:
+The most recently released version of `jfa` can be downloaded from [CRAN](https://cran.r-project.org/package=jfa) by running the following command in R or RStudio:
 
 ```
 install.packages('jfa')

diff --git a/cran-comments.md b/cran-comments.md
@@ -2,6 +2,7 @@
 This is jfa version 0.5.3. In this version I have:
 
 * Updated documentation for all functions.
+* Added several new methods to two existing functions.
 * Fixed an error in the evaluation function.
 
 ## Test environments

diff --git a/inst/CITATION b/inst/CITATION
@@ -4,6 +4,6 @@ bibentry(bibtype = "Manual",
          title = "jfa: Bayesian and Classical Audit Sampling",
          author = person("Koen", "Derks"),
          year = "2021",
-         note = "R package version 0.5.2",
+         note = "R package version 0.5.3",
          url = "https://CRAN.R-project.org/package=jfa",
-		 textVersion = "Derks, K. (2021). jfa: Bayesian and Classical Audit Sampling. R package version 0.5.2.")
+		 textVersion = "Derks, K. (2021). jfa: Bayesian and Classical Audit Sampling. R package version 0.5.3.")
diff --git a/man/auditPrior.Rd b/man/auditPrior.Rd
diff --git a/man/evaluation.Rd b/man/evaluation.Rd
diff --git a/man/figures/readme/downloads/downloads.svg b/man/figures/readme/downloads/downloads.svg
diff --git a/man/figures/render-figures.R b/man/figures/render-figures.R
@@ -20,8 +20,8 @@ xBreaks           <- plotData[['date']]
 yBreaks           <- pretty(c(0, plotData[['count']], max(plotData[['count']]) + 200), n = 6)
 
 # Specify the specific release dates (rounded down)
-releases          <- c('2020-01-01', '2020-08-01', '2020-09-01', '2020-11-01', '2021-01-01', '2021-03-01', '2021-04-01')
-releaseLabs       <- c('0.1.0', '0.2.0', '0.3.0', '0.4.0', '0.5.0', '0.5.1', '0.5.2')
+releases          <- c('2020-01-01', '2020-08-01', '2020-09-01', '2020-11-01', '2021-01-01', '2021-03-01', '2021-04-01', '2021-05-01')
+releaseLabs       <- c('0.1.0', '0.2.0', '0.3.0', '0.4.0', '0.5.0', '0.5.1', '0.5.2', '0.5.3')
 
 # Create the figure
 p <- ggplot2::ggplot(plotData, ggplot2::aes(x = date, y = count)) +

diff --git a/man/manual/jfa_0.5.3.pdf b/man/manual/jfa_0.5.3.pdf
diff --git a/man/planning.Rd b/man/planning.Rd
diff --git a/man/report.Rd b/man/report.Rd
diff --git a/man/selection.Rd b/man/selection.Rd