`sd()`

will compute the standard deviations of the columns, equivalent
to calling `apply(x, MARGIN=2, FUN=sd)`

(which will work for
distributed matrices, by the way). However, this should be much faster and
use less memory than `apply()`

. If `reduce=FALSE`

then the return
is a distributed matrix consisting of one (global) row; otherwise, an
`R`

vector is returned, with ownership of this vector determined by
`proc.dest`

.

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`x` |
numeric distributed matrices. |

`na.rm` |
Logical; if TRUE, then |

`reduce` |
logical or string. See details |

`proc.dest` |
Destination process (or 'all') if a reduction occurs |

Returns a distributed matrix.

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