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documentation.yml
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toc:
- name: 'Basic Descriptive Statistics'
- min
- max
- sum
- sumSimple
- quantile
- product
- name: 'Sorted Basic Descriptive Statistics'
description: |
These are special versions of methods that assume your input
is sorted. This assumptions lets them run a lot faster, usually
in O(1).
- minSorted
- maxSorted
- quantileSorted
- name: 'Measures of central tendency'
description: |
These are different ways to identifying centers or locations of
a distribution.
- mean
- addToMean
- mode
- modeSorted
- modeFast
- median
- medianSorted
- harmonicMean
- geometricMean
- rootMeanSquare
- sampleSkewness
- name: 'Measures of dispersion'
description: |
These are different ways of determining how spread out a distribution is.
- variance
- sampleVariance
- standardDeviation
- sampleStandardDeviation
- medianAbsoluteDeviation
- interquartileRange
- sumNthPowerDeviations
- zScore
- name: 'Similarity'
- sampleCorrelation
- sampleCovariance
- rSquared
- name: 'Linear Regression'
- linearRegression
- linearRegressionLine
- name: 'Randomness'
- shuffle
- shuffleInPlace
- sampleWithReplacement
- sample
- name: 'Classifiers'
- BayesianClassifier
- PerceptronModel
- name: 'Distributions'
- bernoulliDistribution
- binomialDistribution
- poissonDistribution
- chiSquaredDistributionTable
- standardNormalTable
- tTest
- tTestTwoSample
- cumulativeStdNormalProbability
- kernelDensityEstimation
- name: 'Errors'
- errorFunction
- inverseErrorFunction
- probit
- epsilon
- name: 'Breaks'
description: |
Breaks methods split datasets into chunks. Often these are used
for segmentation or visualization of a dataset. A method of computing
breaks that splits data evenly can make for a better choropleth map,
for instance, because each color will be represented equally.
- ckmeans
- equalIntervalBreaks
- name: 'Utilities'
- chunk
- chiSquaredGoodnessOfFit
- epsilon
- factorial
- gamma
- uniqueCountSorted