fix typos in documentation and code comments as well as code

Author: jbytecode

CHANGELOG.md

@@ -1,7 +1,7 @@
 # v0.11.6 (Upcoming Release)
 
 - Update citation of `robhatreg` a.k.a Robust Hat Matrix based Regression Estimator
-
+- Fix typos in code, code comments, and documentation
 
 # v0.11.5
 
@@ -107,7 +107,7 @@
 # v0.9.0
 
 - Add exact argument for LAD. If exact is true then the linear programming based exact solution is found. Otherwise, a GA based search is performed to yield approximate solutions. 
-- Remove dependency of Plots.jl. If Plots.jl is installed and loaded manually, the functionality that uses Plot is autmatically loaded by Requires.jl. Affected functions are `dataimage`, `mveltsplot`, and `bchplot`.
+- Remove dependency of Plots.jl. If Plots.jl is installed and loaded manually, the functionality that uses Plot is automatically loaded by Requires.jl. Affected functions are `dataimage`, `mveltsplot`, and `bchplot`.
 
 
 # v0.8.19

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-## welcome, contributer!
+## welcome, contributor!
 
 Please read the [pull requests](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests) 
-section in GitHub before preparing any feauture for this library.
+section in GitHub before preparing any feature for this library.

docs/src/algorithms.md

@@ -130,7 +130,7 @@ LinRegOutliers.cm97
 LinRegOutliers.quantileregression
 ```
 
-## Theil-Sen estimator for multiple regresion 
+## Theil-Sen estimator for multiple regression 
 ```@docs
 LinRegOutliers.theilsen
 ```

examples.md

@@ -19,7 +19,7 @@ where *β₀* and *β₁* are unknown intercept and slope parameters. In `R` and
 @formula(y ~ x)
 ```
 
-where ```~``` operator seperates the dependent and independent variables. When the model includes more than one regressors, the model can similarly be expressed as
+where ```~``` operator separates the dependent and independent variables. When the model includes more than one regressors, the model can similarly be expressed as
 
 ```julia
 @formula(y ~ x1 + x2 + x3)
@@ -119,7 +119,7 @@ defines color using the Euclidean distance, whereas
 
 uses Mahalanobis distances for determining color values. The default distance metric is Euclidean distance. 
 
-In the example below, the distances between observations are calculated and drawn using corresponding colors. Since the method is for multivariate data, only the desing matrix is used. In other terms, the response vector is omitted. 
+In the example below, the distances between observations are calculated and drawn using corresponding colors. Since the method is for multivariate data, only the design matrix is used. In other terms, the response vector is omitted. 
 
 ```julia
 julia> # Matrix of independent variables of Hawkins & Bradu & Kass data

src/bacon.jl

@@ -55,7 +55,7 @@ It also guarantees that at least m indices are returned and that the selected in
 # Arguments
  - `X`: The multivariate matrix where each row is a data point.
  - `m`: The minimum number of points to include in the subset indices.
- - `distances`: The distances vector used for selecting minumum distance indices.
+ - `distances`: The distances vector used for selecting minimum distance indices.
 """
 function select_subset(X::AbstractMatrix{Float64}, m::Int, distances::Array{Float64})
     rank_x = rank(X)
@@ -242,7 +242,7 @@ Run the BACON algorithm to detect outliers on regression data.
  - `alpha`: The quantile used for cutoff
 
 # Description 
-BACON (Blocked Adaptive Computationally efficient Outlier Nominators) algoritm, defined in the citation below,
+BACON (Blocked Adaptive Computationally efficient Outlier Nominators) algorithm, defined in the citation below,
 has many versions, e.g BACON for multivariate data, BACON for regression etc. Since the design matrix of a
 regression model is multivariate data, BACON for multivariate data is performed in early stages of the algorithm.
 After selecting a clean subset of observations, then a forward search is applied. Observations with high

src/basis.jl

@@ -121,7 +121,7 @@ end
 
     designMatrix(setting)
 
-    Return matrix of independent variables including the variable (ones) of the constanst term for a given regression setting.
+    Return matrix of independent variables including the variable (ones) of the constants term for a given regression setting.
 
 # Arguments
 - `setting::RegressionSetting`: A regression setting object.

src/deepestregression.jl

@@ -11,7 +11,7 @@ using mrfDepth_jll: mrfDepth_jll
 """
 	deepestregression(setting; maxit = 1000)
 
-Estimate Deepest Regression paramaters.
+Estimate Deepest Regression parameters.
 
 
 # Arguments

src/ga.jl

@@ -152,12 +152,12 @@ function ga(
     maxs::AbstractVector{Float64},
     pcross::Float64,
     pmutate::Float64,
-    elitisim::Int,
+    elitism::Int,
     iterations::Int,
 )::Array{RealChromosome,1}
     pop = createPopulation(popsize, chsize, mins, maxs)
     for _ = 1:iterations
-        pop = Generation(pop, fcost, elitisim, pcross, pmutate)
+        pop = Generation(pop, fcost, elitism, pcross, pmutate)
     end
     return pop
 end

src/hadi1994.jl

@@ -45,7 +45,7 @@ Dict{Any,Any} with 3 entries:
 ```
 
 # Reference
-Hadi, Ali S. "A modification of a method for the dedection of outliers in multivariate samples"
+Hadi, Ali S. "A modification of a method for the detection of outliers in multivariate samples"
 Journal of the Royal Statistical Society: Series B (Methodological) 56.2 (1994): 393-396.
 """
 function hadi1994(multivariateData::AbstractMatrix{Float64}; alpha = 0.05)

src/lms.jl

@@ -68,7 +68,7 @@ function lms(X::AbstractMatrix{Float64}, y::AbstractVector{Float64}; iters = not
         iters = minimum([500 * p, 3000])
     end
     bestobjective = Inf
-    bestparamaters = Array{Float64}(undef, p)
+    bestparameters = Array{Float64}(undef, p)
     bestres = Array{Float64}(undef, n)
     indices = collect(1:n)
     kindices = collect(p:n)
@@ -83,7 +83,7 @@ function lms(X::AbstractMatrix{Float64}, y::AbstractVector{Float64}; iters = not
             res = sort!((y .- X * betas) .^ 2.0)
             m2 = res[h]
             if m2 < bestobjective
-                bestparamaters = betas
+                bestparameters = betas
                 bestobjective = m2
                 bestres = y .- X * betas
             end
@@ -94,7 +94,7 @@ function lms(X::AbstractMatrix{Float64}, y::AbstractVector{Float64}; iters = not
     s = 1.4826 * sqrt((1.0 + (5.0 / (n - p))) * bestobjective)
     standardizedres = bestres / s
     d = Dict{String, Any}()
-    d["betas"] = bestparamaters
+    d["betas"] = bestparameters
     d["objective"] = bestobjective
     d["S"] = s
     d["stdres"] = standardizedres