Statistics and Models

Statistics and Models

Statistic/ModelOnlineStat
Univariate Statistics:
MeanMean
VarianceVariance
QuantilesQuantile and P2Quantile
Maximum/MinimumExtrema
Skewness and kurtosisMoments
SumSum
Time Series:
DifferenceDiff
LagLag
Autocorrelation/autocovarianceAutoCov
Tracked historyStatHistory
Multivariate Analysis:
Covariance/correlation matrixCovMatrix
Principal components analysisCovMatrix
K-means clustering (SGD)KMeans
Multiple univariate statisticsGroup
Nonparametric Density Estimation:
Histograms/continuous densityHist and KHist
Approximate order statisticsOrderStats
Count for each unique valueCountMap
Parametric Density Estimation:
BetaFitBeta
CauchyFitCauchy
GammaFitGamma
LogNormalFitLogNormal
NormalFitNormal
MultinomialFitMultinomial
MvNormalFitMvNormal
Statistical Learning:
GLMs with regularizationStatLearn
Logistic regressionStatLearn
Linear SVMsStatLearn
Quantile regressionStatLearn
Absolute loss regressionStatLearn
Distance-weighted discriminationStatLearn
Huber-loss regressionStatLearn
Linear (also ridge) regressionLinReg, LinRegBuilder
Decision TreesFastTree
Random ForestFastForest
Naive Bayes ClassifierNBClassifier
Other:
Statistical BootstrapBootstrap
Approx. count of distinct elementsHyperLogLog
Reservoir samplingReservoirSample
CallbacksCallFun
Big Data VizPartition, IndexedPartition
Collections of Stats:
Applied to same data streamSeries, FTSeries
Applied to different data streamsGroup
Calculated stat by groupGroupBy