diff --git a/lib/node_modules/@stdlib/ndarray/base/README.md b/lib/node_modules/@stdlib/ndarray/base/README.md index f5737b30f9b0..7ffc007d37ac 100644 --- a/lib/node_modules/@stdlib/ndarray/base/README.md +++ b/lib/node_modules/@stdlib/ndarray/base/README.md @@ -58,6 +58,7 @@ var o = ns; - [`broadcastArrayExceptDimensions( arr, shape, dims )`][@stdlib/ndarray/base/broadcast-array-except-dimensions]: broadcast an input ndarray to a target shape while keeping a list of specified dimensions unchanged. - [`broadcastArray( arr, shape )`][@stdlib/ndarray/base/broadcast-array]: broadcast an ndarray to a specified shape. - [`broadcastArrays( arrays )`][@stdlib/ndarray/base/broadcast-arrays]: broadcast ndarrays to a common shape. +- [`broadcastScalarLike( x, value )`][@stdlib/ndarray/base/broadcast-scalar-like]: broadcast a scalar value to an ndarray having the same shape and data type as a provided input ndarray. - [`broadcastScalar( value, dtype, shape, order )`][@stdlib/ndarray/base/broadcast-scalar]: broadcast a scalar value to an ndarray having a specified shape. - [`broadcastShapes( shapes )`][@stdlib/ndarray/base/broadcast-shapes]: broadcast array shapes to a single shape. - [`bufferCtors( dtype )`][@stdlib/ndarray/base/buffer-ctors]: ndarray data buffer constructors. @@ -148,6 +149,8 @@ var o = ns; - [`pop( x, dim, writable )`][@stdlib/ndarray/base/pop]: return an array containing a truncated view of an input ndarray and a view of the last element(s) along a specified dimension. - [`prependSingletonDimensions( x, n, writable )`][@stdlib/ndarray/base/prepend-singleton-dimensions]: prepend singleton dimensions. - [`promoteDataTypes( dtypes )`][@stdlib/ndarray/base/promote-dtypes]: resolve the data type that results from applying promotion rules to a provided list of data types. +- [`quaternaryLoopOrder( shape, stridesX, stridesY, stridesZ, stridesW, stridesU )`][@stdlib/ndarray/base/quaternary-loop-interchange-order]: reorder ndarray dimensions and associated strides for loop interchange. +- [`quinaryLoopOrder( shape, stridesX, stridesY, stridesZ, stridesW, stridesU, stridesV )`][@stdlib/ndarray/base/quinary-loop-interchange-order]: reorder ndarray dimensions and associated strides for loop interchange. - [`removeSingletonDimensions( x )`][@stdlib/ndarray/base/remove-singleton-dimensions]: remove singleton dimensions. - [`reverseDimension( x, dim, writable )`][@stdlib/ndarray/base/reverse-dimension]: return a view of an input ndarray in which the order of elements along a specified dimension is reversed. - [`reverse( x, writable )`][@stdlib/ndarray/base/reverse]: return a view of an input ndarray in which the order of elements along each dimension is reversed. @@ -294,6 +297,8 @@ console.log( objectKeys( ns ) ); [@stdlib/ndarray/base/broadcast-arrays]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-arrays +[@stdlib/ndarray/base/broadcast-scalar-like]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-scalar-like + [@stdlib/ndarray/base/broadcast-scalar]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-scalar [@stdlib/ndarray/base/broadcast-shapes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-shapes @@ -474,6 +479,10 @@ console.log( objectKeys( ns ) ); [@stdlib/ndarray/base/promote-dtypes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/promote-dtypes +[@stdlib/ndarray/base/quaternary-loop-interchange-order]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/quaternary-loop-interchange-order + +[@stdlib/ndarray/base/quinary-loop-interchange-order]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/quinary-loop-interchange-order + [@stdlib/ndarray/base/remove-singleton-dimensions]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/remove-singleton-dimensions [@stdlib/ndarray/base/reverse-dimension]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/reverse-dimension diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/README.md b/lib/node_modules/@stdlib/stats/base/ndarray/README.md index 1f8fcd282391..b5acbbd16b7f 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/README.md +++ b/lib/node_modules/@stdlib/stats/base/ndarray/README.md @@ -62,11 +62,19 @@ The namespace exposes the following APIs: - [`dmeankbn2( arrays )`][@stdlib/stats/base/ndarray/dmeankbn2]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm. - [`dmeanli( arrays )`][@stdlib/stats/base/ndarray/dmeanli]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. - [`dmeanlipw( arrays )`][@stdlib/stats/base/ndarray/dmeanlipw]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm with pairwise summation. +- [`dmeanors( arrays )`][@stdlib/stats/base/ndarray/dmeanors]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using ordinary recursive summation. - [`dmeanpn( arrays )`][@stdlib/stats/base/ndarray/dmeanpn]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a two-pass error correction algorithm. - [`dmeanpw( arrays )`][@stdlib/stats/base/ndarray/dmeanpw]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using pairwise summation. +- [`dmeanstdev( arrays )`][@stdlib/stats/base/ndarray/dmeanstdev]: compute the arithmetic mean and standard deviation of a one-dimensional double-precision floating-point ndarray. +- [`dmeanwd( arrays )`][@stdlib/stats/base/ndarray/dmeanwd]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using Welford's algorithm. +- [`dmediansorted( arrays )`][@stdlib/stats/base/ndarray/dmediansorted]: compute the median value of a sorted one-dimensional double-precision floating-point ndarray. +- [`dmidrange( arrays )`][@stdlib/stats/base/ndarray/dmidrange]: compute the mid-range of a one-dimensional double-precision floating-point ndarray. - [`dmin( arrays )`][@stdlib/stats/base/ndarray/dmin]: compute the minimum value of a one-dimensional double-precision floating-point ndarray. - [`dminabs( arrays )`][@stdlib/stats/base/ndarray/dminabs]: compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray. - [`dminsorted( arrays )`][@stdlib/stats/base/ndarray/dminsorted]: compute the minimum value of a sorted one-dimensional double-precision floating-point ndarray. +- [`dmskmax( arrays )`][@stdlib/stats/base/ndarray/dmskmax]: calculate the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask. +- [`dmskmin( arrays )`][@stdlib/stats/base/ndarray/dmskmin]: calculate the minimum value of a one-dimensional double-precision floating-point ndarray according to a mask. +- [`dmskrange( arrays )`][@stdlib/stats/base/ndarray/dmskrange]: calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask. - [`dnanmax( arrays )`][@stdlib/stats/base/ndarray/dnanmax]: compute the maximum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`dnanmaxabs( arrays )`][@stdlib/stats/base/ndarray/dnanmaxabs]: compute the maximum absolute value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`dnanmean( arrays )`][@stdlib/stats/base/ndarray/dnanmean]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. @@ -74,9 +82,18 @@ The namespace exposes the following APIs: - [`dnanmeanpn( arrays )`][@stdlib/stats/base/ndarray/dnanmeanpn]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a two-pass error correction algorithm. - [`dnanmeanpw( arrays )`][@stdlib/stats/base/ndarray/dnanmeanpw]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation. - [`dnanmeanwd( arrays )`][@stdlib/stats/base/ndarray/dnanmeanwd]: compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using Welford's algorithm. +- [`dnanmidrange( arrays )`][@stdlib/stats/base/ndarray/dnanmidrange]: compute the mid-range of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`dnanmin( arrays )`][@stdlib/stats/base/ndarray/dnanmin]: compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`dnanminabs( arrays )`][@stdlib/stats/base/ndarray/dnanminabs]: compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. +- [`dnanmskmax( arrays )`][@stdlib/stats/base/ndarray/dnanmskmax]: compute the maximum value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`dnanmskmin( arrays )`][@stdlib/stats/base/ndarray/dnanmskmin]: compute the minimum value of a double-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`dnanmskrange( arrays )`][@stdlib/stats/base/ndarray/dnanmskrange]: calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`dnanrange( arrays )`][@stdlib/stats/base/ndarray/dnanrange]: compute the range of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. - [`drange( arrays )`][@stdlib/stats/base/ndarray/drange]: compute the range of a one-dimensional double-precision floating-point ndarray. +- [`dstdev( arrays )`][@stdlib/stats/base/ndarray/dstdev]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray. +- [`dstdevch( arrays )`][@stdlib/stats/base/ndarray/dstdevch]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. +- [`dstdevpn( arrays )`][@stdlib/stats/base/ndarray/dstdevpn]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a two-pass algorithm. +- [`dstdevwd( arrays )`][@stdlib/stats/base/ndarray/dstdevwd]: calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using Welford's algorithm. - [`dztest( arrays )`][@stdlib/stats/base/ndarray/dztest]: compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray. - [`dztest2( arrays )`][@stdlib/stats/base/ndarray/dztest2]: compute a two-sample Z-test for two one-dimensional double-precision floating-point ndarrays. - [`maxBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/max-by]: compute the maximum value of a one-dimensional ndarray via a callback function. @@ -91,20 +108,33 @@ The namespace exposes the following APIs: - [`meanpw( arrays )`][@stdlib/stats/base/ndarray/meanpw]: compute the arithmetic mean of a one-dimensional ndarray using pairwise summation. - [`meanwd( arrays )`][@stdlib/stats/base/ndarray/meanwd]: compute the arithmetic mean of a one-dimensional ndarray using Welford's algorithm. - [`mediansorted( arrays )`][@stdlib/stats/base/ndarray/mediansorted]: compute the median value of a sorted one-dimensional ndarray. +- [`midrangeBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/midrange-by]: calculate the mid-range of a one-dimensional ndarray via a callback function. +- [`midrange( arrays )`][@stdlib/stats/base/ndarray/midrange]: compute the mid-range of a one-dimensional ndarray. - [`minBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/min-by]: compute the minimum value of a one-dimensional ndarray via a callback function. - [`min( arrays )`][@stdlib/stats/base/ndarray/min]: compute the minimum value of a one-dimensional ndarray. - [`minabs( arrays )`][@stdlib/stats/base/ndarray/minabs]: compute the minimum absolute value of a one-dimensional ndarray. - [`minsorted( arrays )`][@stdlib/stats/base/ndarray/minsorted]: compute the minimum value of a sorted one-dimensional ndarray. - [`mskmax( arrays )`][@stdlib/stats/base/ndarray/mskmax]: calculate the maximum value of a one-dimensional ndarray according to a mask. +- [`mskmidrange( arrays )`][@stdlib/stats/base/ndarray/mskmidrange]: calculate the mid-range of a one-dimensional ndarray according to a mask. - [`mskmin( arrays )`][@stdlib/stats/base/ndarray/mskmin]: calculate the minimum value of a one-dimensional ndarray according to a mask. - [`mskrange( arrays )`][@stdlib/stats/base/ndarray/mskrange]: calculate the range of a one-dimensional ndarray according to a mask. +- [`nanmaxBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/nanmax-by]: compute the maximum value of a one-dimensional ndarray via a callback function, ignoring `NaN` values. - [`nanmax( arrays )`][@stdlib/stats/base/ndarray/nanmax]: compute the maximum value of a one-dimensional ndarray, ignoring `NaN` values. - [`nanmaxabs( arrays )`][@stdlib/stats/base/ndarray/nanmaxabs]: compute the maximum absolute value of a one-dimensional ndarray, ignoring `NaN` values. - [`nanmean( arrays )`][@stdlib/stats/base/ndarray/nanmean]: compute the arithmetic mean of a one-dimensional ndarray, ignoring `NaN` values. +- [`nanmeanors( arrays )`][@stdlib/stats/base/ndarray/nanmeanors]: compute the arithmetic mean of a one-dimensional ndarray, ignoring `NaN` values and using ordinary recursive summation. - [`nanmeanpn( arrays )`][@stdlib/stats/base/ndarray/nanmeanpn]: compute the arithmetic mean of a one-dimensional ndarray, ignoring `NaN` values and using a two-pass error correction algorithm. - [`nanmeanwd( arrays )`][@stdlib/stats/base/ndarray/nanmeanwd]: compute the arithmetic mean of a one-dimensional ndarray, ignoring `NaN` values and using Welford's algorithm. +- [`nanmidrangeBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/nanmidrange-by]: calculate the mid-range of a one-dimensional ndarray via a callback function, ignoring `NaN` values. +- [`nanmidrange( arrays )`][@stdlib/stats/base/ndarray/nanmidrange]: compute the mid-range of a one-dimensional ndarray, ignoring `NaN` values. +- [`nanminBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/nanmin-by]: compute the minimum value of a one-dimensional ndarray via a callback function, ignoring `NaN` values. - [`nanmin( arrays )`][@stdlib/stats/base/ndarray/nanmin]: compute the minimum value of a one-dimensional ndarray, ignoring `NaN` values. - [`nanminabs( arrays )`][@stdlib/stats/base/ndarray/nanminabs]: compute the minimum absolute value of a one-dimensional ndarray, ignoring `NaN` values. +- [`nanmskmax( arrays )`][@stdlib/stats/base/ndarray/nanmskmax]: calculate the maximum value of a one-dimensional ndarray according to a mask, ignoring `NaN` values. +- [`nanmskmin( arrays )`][@stdlib/stats/base/ndarray/nanmskmin]: calculate the minimum value of a one-dimensional ndarray according to a mask, ignoring `NaN` values. +- [`nanmskrange( arrays )`][@stdlib/stats/base/ndarray/nanmskrange]: calculate the range of a one-dimensional ndarray according to a mask, ignoring `NaN` values. +- [`nanrangeBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/nanrange-by]: calculate the range of a one-dimensional ndarray via a callback function, ignoring `NaN` values. +- [`nanrange( arrays )`][@stdlib/stats/base/ndarray/nanrange]: compute the range of a one-dimensional ndarray, ignoring `NaN` values. - [`rangeBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/range-by]: calculate the range of a one-dimensional ndarray via a callback function. - [`range( arrays )`][@stdlib/stats/base/ndarray/range]: compute the range of a one-dimensional ndarray. - [`scovarmtk( arrays )`][@stdlib/stats/base/ndarray/scovarmtk]: calculate the covariance of two one-dimensional single-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm. @@ -112,6 +142,9 @@ The namespace exposes the following APIs: - [`scumaxabs( arrays )`][@stdlib/stats/base/ndarray/scumaxabs]: compute the cumulative maximum absolute value of a one-dimensional single-precision floating-point ndarray. - [`scumin( arrays )`][@stdlib/stats/base/ndarray/scumin]: compute the cumulative minimum value of a one-dimensional single-precision floating-point ndarray. - [`scuminabs( arrays )`][@stdlib/stats/base/ndarray/scuminabs]: compute the cumulative minimum absolute value of a one-dimensional single-precision floating-point ndarray. +- [`sdsmean( arrays )`][@stdlib/stats/base/ndarray/sdsmean]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using extended accumulation. +- [`sdsmeanors( arrays )`][@stdlib/stats/base/ndarray/sdsmeanors]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation with extended accumulation. +- [`sdsnanmeanors( arrays )`][@stdlib/stats/base/ndarray/sdsnanmeanors]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation with extended accumulation. - [`smax( arrays )`][@stdlib/stats/base/ndarray/smax]: compute the maximum value of a one-dimensional single-precision floating-point ndarray. - [`smaxabs( arrays )`][@stdlib/stats/base/ndarray/smaxabs]: compute the maximum absolute value of a one-dimensional single-precision floating-point ndarray. - [`smaxabssorted( arrays )`][@stdlib/stats/base/ndarray/smaxabssorted]: compute the maximum absolute value of a sorted one-dimensional single-precision floating-point ndarray. @@ -121,23 +154,48 @@ The namespace exposes the following APIs: - [`smeankbn2( arrays )`][@stdlib/stats/base/ndarray/smeankbn2]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm. - [`smeanli( arrays )`][@stdlib/stats/base/ndarray/smeanli]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm. - [`smeanlipw( arrays )`][@stdlib/stats/base/ndarray/smeanlipw]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm with pairwise summation. +- [`smeanors( arrays )`][@stdlib/stats/base/ndarray/smeanors]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation. - [`smeanpn( arrays )`][@stdlib/stats/base/ndarray/smeanpn]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a two-pass error correction algorithm. - [`smeanpw( arrays )`][@stdlib/stats/base/ndarray/smeanpw]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using pairwise summation. - [`smeanwd( arrays )`][@stdlib/stats/base/ndarray/smeanwd]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using Welford's algorithm. +- [`smediansorted( arrays )`][@stdlib/stats/base/ndarray/smediansorted]: compute the median value of a sorted one-dimensional single-precision floating-point ndarray. +- [`smidrange( arrays )`][@stdlib/stats/base/ndarray/smidrange]: compute the mid-range of a one-dimensional single-precision floating-point ndarray. - [`smin( arrays )`][@stdlib/stats/base/ndarray/smin]: compute the minimum value of a one-dimensional single-precision floating-point ndarray. - [`sminabs( arrays )`][@stdlib/stats/base/ndarray/sminabs]: compute the minimum absolute value of a one-dimensional single-precision floating-point ndarray. - [`sminsorted( arrays )`][@stdlib/stats/base/ndarray/sminsorted]: compute the minimum value of a sorted one-dimensional single-precision floating-point ndarray. +- [`smskmax( arrays )`][@stdlib/stats/base/ndarray/smskmax]: calculate the maximum value of a one-dimensional single-precision floating-point ndarray according to a mask. +- [`smskmidrange( arrays )`][@stdlib/stats/base/ndarray/smskmidrange]: calculate the mid-range of a one-dimensional single-precision floating-point ndarray according to a mask. +- [`smskmin( arrays )`][@stdlib/stats/base/ndarray/smskmin]: calculate the minimum value of a one-dimensional single-precision floating-point ndarray according to a mask. +- [`smskrange( arrays )`][@stdlib/stats/base/ndarray/smskrange]: calculate the range of a one-dimensional single-precision floating-point ndarray according to a mask. - [`snanmax( arrays )`][@stdlib/stats/base/ndarray/snanmax]: compute the maximum value of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanmaxabs( arrays )`][@stdlib/stats/base/ndarray/snanmaxabs]: compute the maximum absolute value of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanmean( arrays )`][@stdlib/stats/base/ndarray/snanmean]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanmeanors( arrays )`][@stdlib/stats/base/ndarray/snanmeanors]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values and using ordinary recursive summation. - [`snanmeanpn( arrays )`][@stdlib/stats/base/ndarray/snanmeanpn]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values and using a two-pass error correction algorithm. - [`snanmeanwd( arrays )`][@stdlib/stats/base/ndarray/snanmeanwd]: compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values and using Welford's algorithm. +- [`snanmidrange( arrays )`][@stdlib/stats/base/ndarray/snanmidrange]: compute the mid-range of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanmin( arrays )`][@stdlib/stats/base/ndarray/snanmin]: compute the minimum value of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`snanminabs( arrays )`][@stdlib/stats/base/ndarray/snanminabs]: compute the minimum absolute value of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. +- [`snanmskmax( arrays )`][@stdlib/stats/base/ndarray/snanmskmax]: calculate the maximum value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`snanmskmin( arrays )`][@stdlib/stats/base/ndarray/snanmskmin]: calculate the minimum value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`snanmskrange( arrays )`][@stdlib/stats/base/ndarray/snanmskrange]: calculate the range of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring `NaN` values. +- [`snanrange( arrays )`][@stdlib/stats/base/ndarray/snanrange]: compute the range of a one-dimensional single-precision floating-point ndarray, ignoring `NaN` values. - [`srange( arrays )`][@stdlib/stats/base/ndarray/srange]: compute the range of a one-dimensional single-precision floating-point ndarray. +- [`sstdev( arrays )`][@stdlib/stats/base/ndarray/sstdev]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray. +- [`sstdevch( arrays )`][@stdlib/stats/base/ndarray/sstdevch]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm. +- [`sstdevpn( arrays )`][@stdlib/stats/base/ndarray/sstdevpn]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a two-pass algorithm. +- [`sstdevwd( arrays )`][@stdlib/stats/base/ndarray/sstdevwd]: calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using Welford's algorithm. +- [`stdev( arrays )`][@stdlib/stats/base/ndarray/stdev]: calculate the standard deviation of a one-dimensional ndarray. +- [`stdevch( arrays )`][@stdlib/stats/base/ndarray/stdevch]: calculate the standard deviation of a one-dimensional ndarray using a one-pass trial mean algorithm. +- [`stdevpn( arrays )`][@stdlib/stats/base/ndarray/stdevpn]: calculate the standard deviation of a one-dimensional ndarray using a two-pass algorithm. +- [`stdevtk( arrays )`][@stdlib/stats/base/ndarray/stdevtk]: calculate the standard deviation of a one-dimensional ndarray using a one-pass textbook algorithm. +- [`stdevwd( arrays )`][@stdlib/stats/base/ndarray/stdevwd]: calculate the standard deviation of a one-dimensional ndarray using Welford's algorithm. +- [`stdevyc( arrays )`][@stdlib/stats/base/ndarray/stdevyc]: calculate the standard deviation of a one-dimensional ndarray using a one-pass algorithm proposed by Youngs and Cramer. - [`sztest( arrays )`][@stdlib/stats/base/ndarray/sztest]: compute a one-sample Z-test for a one-dimensional single-precision floating-point ndarray. - [`sztest2( arrays )`][@stdlib/stats/base/ndarray/sztest2]: compute a two-sample Z-test for two one-dimensional single-precision floating-point ndarrays. +- [`variance( arrays )`][@stdlib/stats/base/ndarray/variance]: calculate the variance of a one-dimensional ndarray. +- [`variancech( arrays )`][@stdlib/stats/base/ndarray/variancech]: calculate the variance of a one-dimensional ndarray using a one-pass trial mean algorithm. +- [`variancewd( arrays )`][@stdlib/stats/base/ndarray/variancewd]: calculate the variance of a one-dimensional ndarray using Welford's algorithm. - [`ztest( arrays )`][@stdlib/stats/base/ndarray/ztest]: compute a one-sample Z-test for a one-dimensional ndarray. - [`ztest2( arrays )`][@stdlib/stats/base/ndarray/ztest2]: compute a two-sample Z-test for two one-dimensional ndarrays. @@ -216,16 +274,32 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/dmeanlipw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmeanlipw +[@stdlib/stats/base/ndarray/dmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmeanors + [@stdlib/stats/base/ndarray/dmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmeanpn [@stdlib/stats/base/ndarray/dmeanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmeanpw +[@stdlib/stats/base/ndarray/dmeanstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmeanstdev + +[@stdlib/stats/base/ndarray/dmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmeanwd + +[@stdlib/stats/base/ndarray/dmediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmediansorted + +[@stdlib/stats/base/ndarray/dmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmidrange + [@stdlib/stats/base/ndarray/dmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmin [@stdlib/stats/base/ndarray/dminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dminabs [@stdlib/stats/base/ndarray/dminsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dminsorted +[@stdlib/stats/base/ndarray/dmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskmax + +[@stdlib/stats/base/ndarray/dmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskmin + +[@stdlib/stats/base/ndarray/dmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmskrange + [@stdlib/stats/base/ndarray/dnanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmax [@stdlib/stats/base/ndarray/dnanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmaxabs @@ -240,12 +314,30 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/dnanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmeanwd +[@stdlib/stats/base/ndarray/dnanmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmidrange + [@stdlib/stats/base/ndarray/dnanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmin [@stdlib/stats/base/ndarray/dnanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanminabs +[@stdlib/stats/base/ndarray/dnanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskmax + +[@stdlib/stats/base/ndarray/dnanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskmin + +[@stdlib/stats/base/ndarray/dnanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanmskrange + +[@stdlib/stats/base/ndarray/dnanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dnanrange + [@stdlib/stats/base/ndarray/drange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/drange +[@stdlib/stats/base/ndarray/dstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdev + +[@stdlib/stats/base/ndarray/dstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevch + +[@stdlib/stats/base/ndarray/dstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevpn + +[@stdlib/stats/base/ndarray/dstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dstdevwd + [@stdlib/stats/base/ndarray/dztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dztest [@stdlib/stats/base/ndarray/dztest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dztest2 @@ -274,6 +366,10 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/mediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/mediansorted +[@stdlib/stats/base/ndarray/midrange-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/midrange-by + +[@stdlib/stats/base/ndarray/midrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/midrange + [@stdlib/stats/base/ndarray/min-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/min-by [@stdlib/stats/base/ndarray/min]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/min @@ -284,24 +380,46 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/mskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/mskmax +[@stdlib/stats/base/ndarray/mskmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/mskmidrange + [@stdlib/stats/base/ndarray/mskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/mskmin [@stdlib/stats/base/ndarray/mskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/mskrange +[@stdlib/stats/base/ndarray/nanmax-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmax-by + [@stdlib/stats/base/ndarray/nanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmax [@stdlib/stats/base/ndarray/nanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmaxabs [@stdlib/stats/base/ndarray/nanmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmean +[@stdlib/stats/base/ndarray/nanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmeanors + [@stdlib/stats/base/ndarray/nanmeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmeanpn [@stdlib/stats/base/ndarray/nanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmeanwd +[@stdlib/stats/base/ndarray/nanmidrange-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmidrange-by + +[@stdlib/stats/base/ndarray/nanmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmidrange + +[@stdlib/stats/base/ndarray/nanmin-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmin-by + [@stdlib/stats/base/ndarray/nanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmin [@stdlib/stats/base/ndarray/nanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanminabs +[@stdlib/stats/base/ndarray/nanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskmax + +[@stdlib/stats/base/ndarray/nanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskmin + +[@stdlib/stats/base/ndarray/nanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanmskrange + +[@stdlib/stats/base/ndarray/nanrange-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanrange-by + +[@stdlib/stats/base/ndarray/nanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/nanrange + [@stdlib/stats/base/ndarray/range-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/range-by [@stdlib/stats/base/ndarray/range]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/range @@ -316,6 +434,12 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/scuminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/scuminabs +[@stdlib/stats/base/ndarray/sdsmean]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sdsmean + +[@stdlib/stats/base/ndarray/sdsmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sdsmeanors + +[@stdlib/stats/base/ndarray/sdsnanmeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sdsnanmeanors + [@stdlib/stats/base/ndarray/smax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smax [@stdlib/stats/base/ndarray/smaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smaxabs @@ -334,18 +458,32 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/smeanlipw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smeanlipw +[@stdlib/stats/base/ndarray/smeanors]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smeanors + [@stdlib/stats/base/ndarray/smeanpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smeanpn [@stdlib/stats/base/ndarray/smeanpw]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smeanpw [@stdlib/stats/base/ndarray/smeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smeanwd +[@stdlib/stats/base/ndarray/smediansorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smediansorted + +[@stdlib/stats/base/ndarray/smidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smidrange + [@stdlib/stats/base/ndarray/smin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smin [@stdlib/stats/base/ndarray/sminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sminabs [@stdlib/stats/base/ndarray/sminsorted]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sminsorted +[@stdlib/stats/base/ndarray/smskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smskmax + +[@stdlib/stats/base/ndarray/smskmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smskmidrange + +[@stdlib/stats/base/ndarray/smskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smskmin + +[@stdlib/stats/base/ndarray/smskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smskrange + [@stdlib/stats/base/ndarray/snanmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmax [@stdlib/stats/base/ndarray/snanmaxabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmaxabs @@ -358,16 +496,52 @@ console.log( objectKeys( ns ) ); [@stdlib/stats/base/ndarray/snanmeanwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmeanwd +[@stdlib/stats/base/ndarray/snanmidrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmidrange + [@stdlib/stats/base/ndarray/snanmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmin [@stdlib/stats/base/ndarray/snanminabs]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanminabs +[@stdlib/stats/base/ndarray/snanmskmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskmax + +[@stdlib/stats/base/ndarray/snanmskmin]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskmin + +[@stdlib/stats/base/ndarray/snanmskrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanmskrange + +[@stdlib/stats/base/ndarray/snanrange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/snanrange + [@stdlib/stats/base/ndarray/srange]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/srange +[@stdlib/stats/base/ndarray/sstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdev + +[@stdlib/stats/base/ndarray/sstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevch + +[@stdlib/stats/base/ndarray/sstdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevpn + +[@stdlib/stats/base/ndarray/sstdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sstdevwd + +[@stdlib/stats/base/ndarray/stdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdev + +[@stdlib/stats/base/ndarray/stdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdevch + +[@stdlib/stats/base/ndarray/stdevpn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdevpn + +[@stdlib/stats/base/ndarray/stdevtk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdevtk + +[@stdlib/stats/base/ndarray/stdevwd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdevwd + +[@stdlib/stats/base/ndarray/stdevyc]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/stdevyc + [@stdlib/stats/base/ndarray/sztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sztest [@stdlib/stats/base/ndarray/sztest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sztest2 +[@stdlib/stats/base/ndarray/variance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variance + +[@stdlib/stats/base/ndarray/variancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variancech + +[@stdlib/stats/base/ndarray/variancewd]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/variancewd + [@stdlib/stats/base/ndarray/ztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/ztest [@stdlib/stats/base/ndarray/ztest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/ztest2