0.2.1 • Published 2 months ago
@stdlib/stats-incr v0.2.1
Incremental Statistics
Incremental statistics.
Installation
npm install @stdlib/stats-incr
Usage
var ns = require( '@stdlib/stats-incr' );
ns
Namespace containing functions for computing statistics incrementally.
var incr = ns;
// returns {...}
incrapcorr( [mx, my] )
: compute a sample absolute Pearson product-moment correlation coefficient incrementally.incrcount()
: compute a count incrementally.incrcovariance( [mx, my] )
: compute an unbiased sample covariance incrementally.incrcovmat( out[, means] )
: compute an unbiased sample covariance matrix incrementally.incrcv( [mean] )
: compute the coefficient of variation (CV) incrementally.increwmean( alpha )
: compute an exponentially weighted mean incrementally.increwstdev( alpha )
: compute an exponentially weighted standard deviation incrementally.increwvariance( alpha )
: compute an exponentially weighted variance incrementally.incrgmean()
: compute a geometric mean incrementally.incrgrubbs( [options] )
: grubbs' test for outliers.incrhmean()
: compute a harmonic mean incrementally.incrkurtosis()
: compute a corrected sample excess kurtosis incrementally.incrmaape()
: compute the mean arctangent absolute percentage error (MAAPE) incrementally.incrmae()
: compute the mean absolute error (MAE) incrementally.incrmapcorr( window[, mx, my] )
: compute a moving sample absolute Pearson product-moment correlation coefficient incrementally.incrmape()
: compute the mean absolute percentage error (MAPE) incrementally.incrmax()
: compute a maximum value incrementally.incrmaxabs()
: compute a maximum absolute value incrementally.incrmcovariance( window[, mx, my] )
: compute a moving unbiased sample covariance incrementally.incrmcv( window[, mean] )
: compute a moving coefficient of variation (CV) incrementally.incrmda()
: compute the mean directional accuracy (MDA) incrementally.incrme()
: compute the mean error (ME) incrementally.incrmean()
: compute an arithmetic mean incrementally.incrmeanabs()
: compute an arithmetic mean of absolute values incrementally.incrmeanabs2()
: compute an arithmetic mean of squared absolute values incrementally.incrmeanstdev( [out] )
: compute an arithmetic mean and a corrected sample standard deviation incrementally.incrmeanvar( [out] )
: compute an arithmetic mean and an unbiased sample variance incrementally.incrmgmean( window )
: compute a moving geometric mean incrementally.incrmgrubbs( window[, options] )
: moving Grubbs' test for outliers.incrmhmean( window )
: compute a moving harmonic mean incrementally.incrmidrange()
: compute a mid-range incrementally.incrmin()
: compute a minimum value incrementally.incrminabs()
: compute a minimum absolute value incrementally.incrminmax( [out] )
: compute a minimum and maximum incrementally.incrminmaxabs( [out] )
: compute minimum and maximum absolute values incrementally.incrmmaape( window )
: compute a moving mean arctangent absolute percentage error (MAAPE) incrementally.incrmmae( window )
: compute a moving mean absolute error (MAE) incrementally.incrmmape( window )
: compute a moving mean absolute percentage error incrementally.incrmmax( window )
: compute a moving maximum value incrementally.incrmmaxabs( window )
: compute a moving maximum absolute value incrementally.incrmmda( window )
: compute a moving mean directional accuracy (MDA) incrementally.incrmme( window )
: compute a moving mean error (ME) incrementally.incrmmean( window )
: compute a moving arithmetic mean incrementally.incrmmeanabs( window )
: compute a moving arithmetic mean of absolute values incrementally.incrmmeanabs2( window )
: compute a moving arithmetic mean of squared absolute values incrementally.incrmmeanstdev( [out,] window )
: compute a moving arithmetic mean and corrected sample standard deviation incrementally.incrmmeanvar( [out,] window )
: compute a moving arithmetic mean and unbiased sample variance incrementally.incrmmidrange( window )
: compute a moving mid-range incrementally.incrmmin( window )
: compute a moving minimum value incrementally.incrmminabs( window )
: compute a moving minimum absolute value incrementally.incrmminmax( [out,] window )
: compute a moving minimum and maximum incrementally.incrmminmaxabs( [out,] window )
: compute moving minimum and maximum absolute values incrementally.incrmmpe( window )
: compute a moving mean percentage error (MPE) incrementally.incrmmse( window )
: compute a moving mean squared error (MSE) incrementally.incrmpcorr( window[, mx, my] )
: compute a moving sample Pearson product-moment correlation coefficient incrementally.incrmpcorr2( window[, mx, my] )
: compute a moving squared sample Pearson product-moment correlation coefficient incrementally.incrmpcorrdist( window[, mx, my] )
: compute a moving sample Pearson product-moment correlation distance incrementally.incrmpe()
: compute the mean percentage error (MPE) incrementally.incrmprod( window )
: compute a moving product incrementally.incrmrange( window )
: compute a moving range incrementally.incrmrmse( window )
: compute a moving root mean squared error (RMSE) incrementally.incrmrss( window )
: compute a moving residual sum of squares (RSS) incrementally.incrmse()
: compute the mean squared error (MSE) incrementally.incrmstdev( window[, mean] )
: compute a moving corrected sample standard deviation incrementally.incrmsum( window )
: compute a moving sum incrementally.incrmsumabs( window )
: compute a moving sum of absolute values incrementally.incrmsumabs2( window )
: compute a moving sum of squared absolute values incrementally.incrmsummary( window )
: compute a moving statistical summary incrementally.incrmsumprod( window )
: compute a moving sum of products incrementally.incrmvariance( window[, mean] )
: compute a moving unbiased sample variance incrementally.incrmvmr( window[, mean] )
: compute a moving variance-to-mean ratio (VMR) incrementally.incrnancount()
: compute a count incrementally, ignoringNaN
values.incrnansum()
: compute a sum incrementally, ignoringNaN
values.incrnansumabs()
: compute a sum of absolute values incrementally, ignoringNaN
values.incrnansumabs2()
: compute a sum of squared absolute values incrementally, ignoringNaN
values.incrpcorr( [mx, my] )
: compute a sample Pearson product-moment correlation coefficient incrementally.incrpcorr2( [mx, my] )
: compute a squared sample Pearson product-moment correlation coefficient incrementally.incrpcorrdist( [mx, my] )
: compute a sample Pearson product-moment correlation distance incrementally.incrpcorrdistmat( out[, means] )
: compute a sample Pearson product-moment correlation distance matrix incrementally.incrpcorrmat( out[, means] )
: compute a sample Pearson product-moment correlation matrix incrementally.incrprod()
: compute a product incrementally.incrrange()
: compute a range incrementally.incrrmse()
: compute the root mean squared error (RMSE) incrementally.incrrss()
: compute the residual sum of squares (RSS) incrementally.incrskewness()
: compute a corrected sample skewness incrementally.incrstdev( [mean] )
: compute a corrected sample standard deviation incrementally.incrsum()
: compute a sum incrementally.incrsumabs()
: compute a sum of absolute values incrementally.incrsumabs2()
: compute a sum of squared absolute values incrementally.incrsummary()
: compute a statistical summary incrementally.incrsumprod()
: compute a sum of products incrementally.incrvariance( [mean] )
: compute an unbiased sample variance incrementally.incrvmr( [mean] )
: compute a variance-to-mean ratio (VMR) incrementally.incrwmean()
: compute a weighted arithmetic mean incrementally.
Examples
var getKeys = require( '@stdlib/utils-keys' );
var ns = require( '@stdlib/stats-incr' );
console.log( getKeys( ns ) );
Notice
This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
Community
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.