@stdlib/stats-base-nanmeanpn
Calculate the arithmetic mean of a strided array, ignoring NaN values and using a two-pass error correction algorithm.
Calculate the arithmetic mean of a strided array, ignoring NaN values and using a two-pass error correction algorithm.
Calculate the arithmetic mean of a strided array, ignoring NaN values and using Welford's algorithm.
Calculate the minimum value of a strided array, ignoring NaN values.
Calculate the minimum value of a strided array via a callback function, ignoring NaN values.
Calculate the minimum absolute value of a strided array, ignoring NaN values.
Calculate the maximum value of a strided array according to a mask, ignoring NaN values.
Calculate the minimum value of a strided array according to a mask, ignoring NaN values.
Calculate the range of a strided array according to a mask, ignoring NaN values.
Calculate the range of a strided array, ignoring NaN values.
Calculate the range of a strided array via a callback function, ignoring NaN values.
Calculate the standard deviation of a strided array ignoring NaN values.
Calculate the standard deviation of a strided array ignoring NaN values and using a one-pass trial mean algorithm.
Calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.
Calculate the minimum absolute value of a double-precision floating-point strided array, ignoring NaN values.
Calculate the maximum value of a double-precision floating-point strided array according to a mask, ignoring NaN values.
Calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring NaN values.
Calculate the range of a double-precision floating-point strided array according to a mask, ignoring NaN values.
Calculate the range of a double-precision floating-point strided array, ignoring NaN values.
Calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values.
Calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.