Statistics and Models

Univariate Statistics

Statistic	OnlineStat
Mean	`Mean`
Variance	`Variance`
Quantiles	`Quantile` and `P2Quantile`
Maximum/Minimum	`Extrema`
Skewness and kurtosis	`Moments`
Sum	`Sum`

Data Visualization (See Data Viz)

Note that many OnlineStats also have Plot recipes.

Plot	OnlineStat
Big Data Viz	`Partition`, `IndexedPartition`, `KIndexedPartition`
Mosaic Plot	`Mosaic`
HeatMap	`HeatMap`

Time Series

Statistic	OnlineStat
Difference	`Diff`
Lag	`Lag`
Autocorrelation/autocovariance	`AutoCov`
Tracked history	`StatLag`

Multivariate Analysis

Statistic/Model	OnlineStat
Covariance/correlation matrix	`CovMatrix`
Principal components analysis	`CovMatrix`, `CCIPCA`
K-means clustering	`KMeans`
Multiple univariate statistics	`Group`

Nonparametric Density Estimation

Statistic/Model	OnlineStat
Histograms/continuous density	`Hist`, `KHist`, and `ExpandingHist`
ASH KDE	`Ash`
Approximate order statistics	`OrderStats`
Count for each unique value	`CountMap`
Approximate CDF	`OrderStats`

Parametric Density Estimation

Distribution	OnlineStat
Beta	`FitBeta`
Cauchy	`FitCauchy`
Gamma	`FitGamma`
LogNormal	`FitLogNormal`
Normal	`FitNormal`
Multinomial	`FitMultinomial`
MvNormal	`FitMvNormal`

Statistical Learning

Model	OnlineStat
Linear (also ridge) regression	`LinReg`, `LinRegBuilder`
Decision Trees	`FastTree`
Random Forest	`FastForest`
Naive Bayes Classifier	`NBClassifier`

Other

Statistic/Model	OnlineStat
Handling Missing Data	`FTSeries`, `CountMissing`
Statistical Bootstrap	`Bootstrap`
Approx. count of distinct elements	`HyperLogLog`
Random sample	`ReservoirSample`
Moving Window	`MovingWindow`, `MovingTimeWindow`

Collection of Stats

Statistic/Model	OnlineStat
Univariate data stream	`Series`, `FTSeries`
Multivariate data streams	`Group`
Group by categorical variable	`GroupBy`