replace Array with Vector for one dimensional arrays

Author: jbytecode

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 - Update citation of `robhatreg` a.k.a Robust Hat Matrix based Regression Estimator
 - Fix typos in code, code comments, and documentation
+- Replace one dimensional `Array{` definitions with `Vector{`
 
 # v0.11.5
 

src/bacon.jl

@@ -57,7 +57,7 @@ It also guarantees that at least m indices are returned and that the selected in
  - `m`: The minimum number of points to include in the subset indices.
  - `distances`: The distances vector used for selecting minimum distance indices.
 """
-function select_subset(X::AbstractMatrix{Float64}, m::Int, distances::Array{Float64})
+function select_subset(X::AbstractMatrix{Float64}, m::Int, distances::Vector{Float64})
     rank_x = rank(X)
     sorted_distances = sortperm(distances)
     subset = sorted_distances[1:m]
@@ -152,7 +152,7 @@ This function computes the t distance for each point and returns the distance ve
  - `y`: The output vector
  - `subset`: The vector which denotes the points inside the subset, used to scale the residuals accordingly.
 """
-function compute_t_distance(X::AbstractMatrix{Float64}, y::Array{Float64}, subset::Array{Int64})
+function compute_t_distance(X::AbstractMatrix{Float64}, y::Vector{Float64}, subset::Vector{Int64})
 
     n, p = size(X)
 
@@ -194,7 +194,7 @@ This function computes the initial subset having at least m elements which are l
 """
 function bacon_regression_initial_subset(
     X::AbstractMatrix{Float64},
-    y::Array{Float64},
+    y::Vector{Float64},
     m::Int;
     method::String = "mahalanobis",
     alpha = 0.025,
@@ -266,7 +266,7 @@ Computational statistics & data analysis 34.3 (2000): 279-298.
 """
 function bacon(
     X::AbstractMatrix{Float64},
-    y::Array{Float64};
+    y::Vector{Float64};
     m::Int,
     method::String = "mahalanobis",
     alpha = 0.025,

src/cm97.jl

@@ -56,8 +56,8 @@ function cm97(X::AbstractMatrix{Float64}, y::AbstractVector{Float64}; maxiter::I
     pbar::Float64 = p / n
     hat = hatmatrix(X)
 
-    w_is = Array{Float64}(undef, n)
-    betas = Array{Float64}(undef, p)
+    w_is = Vector{Float64}(undef, n)
+    betas = Vector{Float64}(undef, p)
 
     converged::Bool = false
     iter::Int = 0

src/hadi1992.jl

@@ -115,7 +115,7 @@ function hadi1992(multivariateData::AbstractMatrix{Float64}; alpha = 0.05)
     basic_subset = Int[]
     sorted_mah1 = Float64[]
 
-    Cb = Array{Float64}(undef, p)
+    Cb = Vector{Float64}(undef, p)
     Sb = Matrix{Float64}(undef, p, p)
     newSb = Matrix{Float64}(undef, p, p)
 

src/lms.jl

@@ -68,12 +68,12 @@ function lms(X::AbstractMatrix{Float64}, y::AbstractVector{Float64}; iters = not
         iters = minimum([500 * p, 3000])
     end
     bestobjective = Inf
-    bestparameters = Array{Float64}(undef, p)
-    bestres = Array{Float64}(undef, n)
+    bestparameters = Vector{Float64}(undef, p)
+    bestres = Vector{Float64}(undef, n)
     indices = collect(1:n)
     kindices = collect(p:n)
-    betas = Array{Float64}(undef, p)
-    res = Array{Float64}(undef, n)
+    betas = Vector{Float64}(undef, p)
+    res = Vector{Float64}(undef, n)
 
     for _ = 1:iters
         try

src/mve.jl

@@ -20,9 +20,9 @@ function enlargesubset(initialsubset, data::AbstractMatrix, h::Int)
 
     basicsubset = copy(initialsubset)
 
-    meanvector = Array{Float64}(undef, p)
+    meanvector = Vector{Float64}(undef, p)
 
-    md2sortedindex = Array{Int}(undef, n)
+    md2sortedindex = Vector{Int}(undef, n)
 
     while length(basicsubset) < h
         applyColumns!(meanvector, mean, data[basicsubset, :])
@@ -55,14 +55,14 @@ function robcov(data::Matrix; alpha=0.01, estimator=:mve)
 
     maxiter = minimum([p * 500, 3000])
 
-    initialsubset = Array{Int}(undef, k)
-    bestinitialsubset = Array{Int}(undef, k)
+    initialsubset = Vector{Int}(undef, k)
+    bestinitialsubset = Vector{Int}(undef, k)
 
-    hsubset = Array{Int}(undef, h)
-    besthsubset = Array{Int}(undef, h)
+    hsubset = Vector{Int}(undef, h)
+    besthsubset = Vector{Int}(undef, h)
 
 
-    meanvector = Array{Float64}(undef, p)
+    meanvector = Vector{Float64}(undef, p)
     fill!(meanvector, 0.0)
 
 

src/smr98.jl

@@ -10,7 +10,7 @@ import ..Basis:
 import Distributions: mean, std
 import ..OrdinaryLeastSquares: ols, residuals, predict, coef
 
-function distances(resids::AbstractVector{Float64}, fitteds::Array{Float64})::AbstractMatrix{Float64}
+function distances(resids::AbstractVector{Float64}, fitteds::Vector{Float64})::AbstractMatrix{Float64}
     n = length(resids)
     d = zeros(Float64, n, n)
     for i = 1:n