Portability  GHC 

Stability  experimental 
Maintainer  bos@serpentine.com 
Analysis code for benchmarks.
 data Outliers = Outliers {
 samplesSeen :: !Int64
 lowSevere :: !Int64
 lowMild :: !Int64
 highMild :: !Int64
 highSevere :: !Int64
 data OutlierVariance
 = Unaffected
  Slight
  Moderate
  Severe
 analyseMean :: Sample > Int > Criterion Double
 countOutliers :: Outliers > Int64
 classifyOutliers :: Sample > Outliers
 noteOutliers :: Outliers > Criterion ()
 outlierVariance :: Estimate > Estimate > Double > (OutlierVariance, Double)
Documentation
Outliers from sample data, calculated using the boxplot technique.
Outliers  

data OutlierVariance Source
A description of the extent to which outliers in the sample data affect the sample mean and standard deviation.
Unaffected  Less than 1% effect. 
Slight  Between 1% and 10%. 
Moderate  Between 10% and 50%. 
Severe  Above 50% (i.e. measurements are useless). 
Display the mean of a Sample
, and characterise the outliers
present in the sample.
countOutliers :: Outliers > Int64Source
Count the total number of outliers in a sample.
classifyOutliers :: Sample > OutliersSource
Classify outliers in a data set, using the boxplot technique.
:: Estimate  Bootstrap estimate of sample mean. 
> Estimate  Bootstrap estimate of sample standard deviation. 
> Double  Number of original iterations. 
> (OutlierVariance, Double) 
Compute the extent to which outliers in the sample data affect the sample mean and standard deviation.