Statistics and Models

# Statistics and Models

Statistic/ModelOnlineStat
Univariate Statistics:
Mean`Mean`
Variance`Variance`
Quantiles`Quantile` and `P2Quantile`
Maximum/Minimum`Extrema`
Skewness and kurtosis`Moments`
Sum`Sum`
Time Series:
Difference`Diff`
Lag`Lag`
Autocorrelation/autocovariance`AutoCov`
Tracked history`StatHistory`
Multivariate Analysis:
Covariance/correlation matrix`CovMatrix`
Principal components analysis`CovMatrix`
K-means clustering (SGD)`KMeans`
Multiple univariate statistics`Group`
Nonparametric Density Estimation:
Histograms`Hist`
Approximate order statistics`OrderStats`
Count for each unique value`CountMap`
Parametric Density Estimation:
Beta`FitBeta`
Cauchy`FitCauchy`
Gamma`FitGamma`
LogNormal`FitLogNormal`
Normal`FitNormal`
Multinomial`FitMultinomial`
MvNormal`FitMvNormal`
Statistical Learning:
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`
Other:
Statistical Bootstrap`Bootstrap`
Approx. count of distinct elements`HyperLogLog`
Reservoir sampling`ReservoirSample`
Callbacks`CallFun`, `eachrow`, `eachcol`
Big Data Viz`Partition`, `IndexedPartition`
Collections of Stats:
Applied to same data stream`Series`, `FTSeries`
Applied to different data streams`Group`
Calculated stat by group`GroupBy`