Statistics and Models

Univariate Statistics

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

Time Series

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

Multivariate Analysis

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

Nonparametric Density Estimation

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

Parametric Density Estimation

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

Statistical Learning

Statistic/Model	OnlineStat
GLMs with regularization	`StatLearn`
Logistic regression	`StatLearn`
Linear SVMs	`StatLearn`
Quantile regression	`StatLearn`
Absolute loss regression	`StatLearn`
Distance-weighted discrimination	`StatLearn`
Huber-loss regression	`StatLearn`
Linear (also ridge) regression	`LinReg`, `LinRegBuilder`
Decision Trees	`FastTree`
Random Forest	`FastForest`
Naive Bayes Classifier	`NBClassifier`

Other

Statistic/Model	OnlineStat
Statistical Bootstrap	`Bootstrap`
Approx. count of distinct elements	`HyperLogLog`
Reservoir sampling	`ReservoirSample`
Big Data Viz	`Partition`, `IndexedPartition`

Collection of Stats

Statistic/Model	OnlineStat
Apply stats to same data stream	`Series`, `FTSeries`
Apply stats to different data streams	`Group`
Calculate stat by group	`GroupBy`