VectorizedStatistics

vcor(X::AbstractMatrix; dims::Int=1)

Compute the (Pearson's product-moment) correlation matrix of the matrix X, along dimension dims. As Statistics.cor, but vectorized.

vcor(x::AbstractVector, y::AbstractVector)

Compute the (Pearson's product-moment) correlation between the vectors x and y. As Statistics.cor, but vectorized.

Equivalent to cov(x,y) / (std(x) * std(y)).

vcov(X::AbstractMatrix; dims::Int=1, corrected::Bool=true)

Compute the covariance matrix of the matrix X, along dimension dims. As Statistics.cov, but vectorized.

If corrected is true as is the default, Bessel's correction will be applied, such that the sum is scaled by n-1 rather than n, where n = length(x).

vcov(x::AbstractVector, y::AbstractVector; corrected::Bool=true)

Compute the covariance between the vectors x and y. As Statistics.cov, but vectorized.

If corrected is true as is the default, Bessel's correction will be applied, such that the sum is scaled by n-1 rather than n, where n = length(x).

vextrema(A; dims)

Find the maximum and minimum of A, optionally along the dimensions specified by dims. As Base.extrema, but vectorized.

Examples

julia> A = reshape(Vector(1:2:16), (2,2,2)) 2×2×2 Array{Int64, 3}: [:, :, 1] = 1 5 3 7

[:, :, 2] = 9 13 11 15

julia> extrema(A, dims = (1,2)) 1×1×2 Array{Tuple{Int64, Int64}, 3}: [:, :, 1] = (1, 7)

[:, :, 2] = (9, 15)

vmaximum(A; dims)

Find the greatest value contained in A, optionally over dimensions specified by dims. As Base.maximum, but vectorized.

Examples

julia> using VectorizedStatistics

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> vmaximum(A, dims=1)
1×2 Matrix{Int64}:
 3  4

julia>  vmaximum(A, dims=2)
 2×1 Matrix{Int64}:
 2
 4

vmean(A; dims)

Compute the mean of all elements in A, optionally over dimensions specified by dims. As Statistics.mean, but vectorized.

Examples

julia> using VectorizedStatistics

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> vmean(A, dims=1)
1×2 Matrix{Float64}:
 2.0  3.0

julia> vmean(A, dims=2)
2×1 Matrix{Float64}:
 1.5
 3.5

vminimum(A; dims)

Find the least value contained in A, optionally over dimensions specified by dims. As Base.minimum, but vectorized

Examples

julia> using VectorizedStatistics

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> vminimum(A, dims=1)
1×2 Matrix{Int64}:
 1  2

julia> vminimum(A, dims=2)
 2×1 Matrix{Int64}:
 1
 3

vstd(A; dims=:, mean=nothing, corrected=true)

Compute the variance of all elements in A, optionally over dimensions specified by dims. As Statistics.var, but vectorized.

A precomputed mean may optionally be provided, which results in a somewhat faster calculation. If corrected is true, then Bessel's correction is applied, such that the sum is divided by n-1 rather than n.

Examples

julia> using VectorizedStatistics

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> vstd(A, dims=1)
1×2 Matrix{Float64}:
 1.41421  1.41421

julia> vstd(A, dims=2)
2×1 Matrix{Float64}:
 0.7071067811865476
 0.7071067811865476

vsum(A; dims)

Summate the values contained in A, optionally over dimensions specified by dims. As Base.sum, but vectorized.

Examples

julia> using VectorizedStatistics

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> vsum(A, dims=1)
1×2 Matrix{Int64}:
 4  6

julia> vsum(A, dims=2)
2×1 Matrix{Int64}:
 3
 7

vvar(A; dims=:, mean=nothing, corrected=true)

Compute the variance of all elements in A, optionally over dimensions specified by dims. As Statistics.var, but vectorized.

A precomputed mean may optionally be provided, which results in a somewhat faster calculation. If corrected is true, then Bessel's correction is applied, such that the sum is divided by n-1 rather than n.

Examples

julia> using VectorizedStatistics

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> vvar(A, dims=1)
1×2 Matrix{Float64}:
 2.0  2.0

julia> vvar(A, dims=2)
2×1 Matrix{Float64}:
 0.5
 0.5