The existing algorithm for `column_variance` uses the textbook
formula (`E[X^2]` - E[X]^2), which is well-established to have
numerical issues. While the intention (traversal
of the elements in column-major order) of the extant algorithm
is apparent, we should not sacrifice precision when we do not need to
-- the two-pass algorithm for variance (N.B. the existing algorithm is
already a two-pass algorithm, anyway) using the formula `E[(x -
E[x])(x - E[x]])` can be substituted without issue.
Notably, the other variance implementations in the `statistics`
module use `E[(x -E[x])(x - E[x]])`. Loss of precision aside,
keeping the existing implementation of `column_variance`
causes the obvious absurdity:
```rust
use nalgebra::Matrix2x3;
let m = Matrix2x3::new(1.0, 2.0, 3.0, 4.0, 5.0, 6.0);
assert_ne!(m.column_variance().transpose(), m.transpose().row_variance());
```
We can eliminate both the loss of precision the glaring inconsistency
by switching to the implementation provided by this PR.
For a comprehensive analysis of variance algorithms, see this
[reference](https://ds.ifi.uni-heidelberg.de/files/Team/eschubert/publications/SSDBM18-covariance-authorcopy.pdf),
in particular, Table 2. The "two-pass" described in the paper is the
implementation given in this PR. In terms of simplicity (hence, easier
to maintain), "two-pass" is a suitable choice; in terms of runtime
performance and precision, it is a good balance (c.f. Youngs & Cramer
and "textbook"). Furthermore, it is consistent with the variance
algorithm used in the other "*variance" algorithms in the `statistics`
module.
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