Description Usage Arguments Value Examples
This function validates a dataset using Benford's Law. Its main purposes are to find out where the dataset deviates from Benford's Law and to identify suspicious data that need further verification.
For a more complete example, see the package help at benford.analysis.
1 
data 
a numeric vector. 
number.of.digits 
how many first digits to analyse . 
sign 
The default value for sign is "positive" and it analyzes only data greater than zero. There are also the options "negative" and "both" that will analyze only negative values or both positive and negative values of the data, respectively. For large datasets with both positive and negative numbers, it is usually recommended to perform a separate analysis for each group, for the incentives to manipulate the numbers are usually different. 
discrete 
most real data  like population numbers or accounting data  are discrete, so the default is TRUE. This paramater sets rounding to the differences of the ordered data to avoid floating point number errors in the second order distribution, that usually occurs when data is discrete and the ordered numbers are very close to each other. If your data is continuous (like a simulated lognormal) you should run with discrete = FALSE. 
round 
it defines the number of digits that the rounding will use if discrete = TRUE. 
An object of class Benford containing the results of the analysis. It is a list of eight objects, namely:
info 
general information, including

data 
a data frame with:

s.o.data 
a data frame with:

bfd 
a data frame with:

mantissa 
a data frame with:

MAD 
the mean absolute deviation. 
distortion.factor 
the distortion factor 
stats 
list of "htest" class statistics:

1 2 3 4  data(corporate.payment) #loads data
bfd.cp < benford(corporate.payment$Amount) #generates benford object
bfd.cp #prints
plot(bfd.cp) #plots

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