Statisticians often work with standardized matrices. If `x`

is a data matrix with observations in rows, we want to work with `z = StatsBase.zscore(x, 1)`

. This package defines a `StandardizedMatrix`

type that treats a matrix as standardized without copying or changing data in place.

Suppose our original matrix is sparse and we want to perform matrix-vector multiplication with a standardized version. Typically, standardizing a sparse matrix destroys the sparsity.

```
using StatsBase, BenchmarkTools, StandardizedMatrices, SparseArrays, Statistics
# generate some data
n, p = 100_000, 1000
x = sprandn(n, p, .01)
β = randn(p)
xdense = zscore(x, 1) # this destroys the sparsity
z = StandardizedMatrix(x) # this acts as standardized, but keeps sparse benefits
b1 = @benchmark xdense * β
b2 = @benchmark z * β
ratio(median(b1), median(b2)) # StandardizedMatrix is roughly 13 times faster
```

`*()`

`mul!(Y, A::StandardizedMatrix, B)`

`mul!(Y, A::Adjoint{<:StandardizedMatrix}, B)`