Base (i.e., lower-level) statistical functions.
var stats = require( '@stdlib/stats/base' );Namespace containing "base" (i.e., lower-level) statistical functions.
var ns = stats;
// returns {...}The namespace contains the following sub-namespaces:
dists: base (i.e., lower-level) probability distribution modules.
The namespace contains the following statistical functions:
cumax( N, x, strideX, y, strideY ): calculate the cumulative maximum of a strided array.cumaxabs( N, x, strideX, y, strideY ): calculate the cumulative maximum absolute value of a strided array.cumin( N, x, strideX, y, strideY ): calculate the cumulative minimum of a strided array.cuminabs( N, x, strideX, y, strideY ): calculate the cumulative minimum absolute value of a strided array.ndarray: base ndarray statistical functions.sdsnanmean( N, x, stride ): calculate the arithmetic mean of a single-precision floating-point strided array, ignoringNaNvalues and using extended accumulation.snanstdev( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues.snanstdevch( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass trial mean algorithm.snanstdevpn( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using a two-pass algorithm.snanstdevtk( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass textbook algorithm.snanstdevwd( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using Welford's algorithm.snanstdevyc( N, correction, x, stride ): calculate the standard deviation of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass algorithm proposed by Youngs and Cramer.snanvariance( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues.snanvariancech( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass trial mean algorithm.snanvariancepn( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a two-pass algorithm.snanvariancetk( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass textbook algorithm.snanvariancewd( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using Welford's algorithm.snanvarianceyc( N, correction, x, stride ): calculate the variance of a single-precision floating-point strided array ignoringNaNvalues and using a one-pass algorithm proposed by Youngs and Cramer.
var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/stats/base' );
console.log( objectKeys( ns ) );