úÎ30)      !"#$%&'(SafeB` H $Most common statistics are averages.,online takes a function and turns it into a )G where the step is an incremental update of the (isomorphic) statistic. moving average with a decay ratemso 'ma 1' is the simple average (no decay in the statistic), and 'ma 0.00001' is the last value (insta-decay)L.fold (ma 1) [0..100]50.0 L.fold (ma 1e-12) [0..100] "H 100TrueL.fold (ma 0.9) [0..100]91.00241448887785absolute averageaverage squarestandard deviation|The formulae for standard deviation, expressed in online terminology, highlights how this statistic is composed of averages: 9(\s ss -> sqrt (ss - s ** (one+one))) <$> ma r <*> sqma r8The average deviation of the numbers 1..1000 is about 1  sqrt 12 * 1000 (see <<https:en.wikipedia.org?wiki/Uniform_distribution_(continuous)#Standard_uniform wiki>>)L.fold (std 1) [0..1000]288.9636655359978*The average deviation with a decay of 0.99L.fold (std 0.99) [0..1000]99.28328803164005Dthe covariance of a tuple given an underlying central tendency fold/correlation of a tuple, specialised to Guassian/a generalised version of correlation of a tuple \the beta in a simple linear regression of a tuple given an underlying central tendency fold the alpha of a tuple Ýautocorrelation is a slippery concept. This method starts with the concept that there is an underlying random error process (e), and autocorrelation is a process on top of that ie for a one-step correlation relationship.valuet = e t + k * e@t-1where k is the autocorrelation.ãThere are thus two online rates needed: one for the average being considered to be the dependent variable, and one for the online of the correlation calculation between the most recent value and the moving average. For example, L.fold (autocorr zero one)€would estimate the one-step autocorrelation relationship of the previous value and the current value over the entire sample set. a constant fold   *+SafeB`*¦ oA rough Median. The average absolute value of the stat is used to callibrate estimate drift towards the median/onlineL1' takes a function and turns it into a )M where the step is an incremental update of an (isomorphic) median statistic..onlineL1 takes a function and turns it into a )M where the step is an incremental update of an (isomorphic) median statistic.Bmoving median >>> L.fold (maL1 inc d r) [1..n] 93.92822312742108moving absolute deviationcovariance of a tuplecorrelation of a tuple1the beta in a simple linear regression of a tuple+the alpha in a simple linear regression of , on -None-B`.'!a raw non-online tdigest fold"non-online version#non-online version$/decaying quantiles based on the tdigest library'/decaying histogram based on the tdigest library  !"#$%&' !"# $%&' NoneB`.ì'  !"#$%&'SafeB`/à./0123456      !!"#$%&'()*+,-./012312456789:;<=%online-0.2.1.0-LuR9Ag8d3bXFYl4XE8RMzsOnline.AveragesOnline.MediansOnline.QuantilesOnline Paths_onlineAverageronlinemaabsmasqmastdcov corrGausscorrbetaalphaautocorrmconst$fMonoidAveragerMedianer medAbsSummedCount medianEst onlineL1'onlineL1maL1absmaL1covL1corrL1betaL1alphaL1 autocorrL1 OnlineTDigesttdtdNtdRatetDigesttDigestQuantiles tDigestHistonlineQuantilesmedianonlineDigitizeonlineDigestHist$fShowOnlineTDigest"foldl-1.3.5-9SbS5P6vByr7GpgUL45Af5 Control.FoldlFold _averagerbase Data.Tuplesndfstversion getBinDir getLibDir getDynLibDir getDataDir getLibexecDir getSysconfDirgetDataFileName