# MUS-package: Monetary Unit Sampling and Estimation Methods, Widely Used in... In MUS: Monetary Unit Sampling and Estimation Methods, Widely Used in Auditing

## Description

Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.

## Details

Monetary Unit Sampling (MUS), also known as Dollar Unit Sampling (DUS) or Probability-Proportional-to-Size Sampling (PPS), is a sampling approach that is widely used in auditing.

This package was written mainly for a research project. However, it should be possible to use the methods for practical auditing, too. Furthermore, the package comes with ABSOLUTELY NO WARRANTY. Use it at your own risk!

You have to walk through four steps: 1. Plan a sample and determine the sample size, use function: MUS.planning 2. Extract the sample, use function: MUS.extract 3. Audit the extracted sample (e.g. by asking for debtor confirmations). 4. Evaluate the audited sample, use function: MUS.evaluation

## Author(s)

Henning Prömpers, André Guimarães Maintainer: Henning Prömpers <henning@proempers.net>

`MUS.planning` for planning a sample, `MUS.extraction` for extraction of the planned sample and `MUS.evaluation` for evaluation of the extracted and audited sample.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## Simple Example library(MUS) # Assume 500 invoices, each between 1 and 1000 monetary units example.data.1 <- data.frame(book.value=round(runif(n=500, min=1, max=1000))) # Plan a sample and cache it plan.results.simple <- MUS.planning(data=example.data.1, tolerable.error=100000, expected.error=20000) # Extract a sample and cache it (no high values exist in this example) extract.results.simple <- MUS.extraction(plan.results.simple) # Copy book values into a new column audit values audited.sample.simple <- extract.results.simple\$sample audited.sample.simple <- cbind(audited.sample.simple, audit.value=audited.sample.simple\$book.value) # Edit manually (if any audit difference occur) #audited.sample.simple <- edit(audited.sample.simple) # Evaluate the sample, cache and print it evaluation.results.simple <- MUS.evaluation(extract.results.simple, audited.sample.simple) print(evaluation.results.simple) ```

### Example output

```MONETARY UNIT SAMPLING

The sample provides a reasonable basis to conclude that the population
is free of material misstatements (given the parameters below).

The conclusion is based on a calculated Upper Error Limit of 70234 for
overstatements and 70234 for understatements (please be aware that MUS
is not designed to detect understatements, thus they can only be used
as an indicator).

Most important parameters:
- Confidence Level:				0.95
- Tolerable Error (Materiality):		1e+05
- Population gross value:			248071
- Expected Error in population:			20000
- Sample size:					9
- Threshold for individual significant items:	27563

Projected Misstatement:
No misstatements found. Thus, the projected misstatememt is 0.
```

MUS documentation built on May 2, 2019, 12:36 p.m.