Spellcheck

.github/workflows/.typos.toml

@@ -0,0 +1,6 @@
+[default.extend-words]
+Missings = "Missings"
+devide = "divide"
+exluding = "excluding"
+
+

.github/workflows/SpellCheck.yml

@@ -0,0 +1,14 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v4
+      - name: Check spelling
+        uses: crate-ci/typos@master
+

README.md

@@ -19,7 +19,7 @@ to
 
 This can also be used to approximate one distribution via a different distribution by matching its moments.
 
-User needs to [explicitly using Optim.jl](https://bgctw.github.io/DistributionFits.jl/stable/set_optimize/) for DitributionFits.jl to work properly:
+User needs to [explicitly using Optim.jl](https://bgctw.github.io/DistributionFits.jl/stable/set_optimize/) for DistributionFits.jl to work properly:
 ```julia
 using DistributionFits, Optim
 ```

docs/src/logitnormal.md

@@ -29,14 +29,14 @@ However, user might have an idea of the spread, or the inverse: peakedness,
 of the distribution.
 
 With increasing spread, the logitnormal distribution becomes bimodal.
-The following functiion estimates the most spread, i.e most
+The following function estimates the most spread, i.e most
 flat distribution that has a single mode at the given location.
 
 ```@docs
 fit_mode_flat
 ```
 
-The found maximum spread parameter, σ, is devided by the peakedness
+The found maximum spread parameter, σ, is divided by the peakedness
 argument to specify distributions given the mode that are more
 peaked.
 

docs/src/partype.md

@@ -18,7 +18,7 @@ partype(d) == Float32
 true
 ```
 
-## Infering the parametric type from other arguments.
+## Inferring the parametric type from other arguments.
 
 If the parametric type is omitted, default Float64 is assumed, or inferred from
 other parameters of the fitting function.

jmd/fitModeFlat.jmd

@@ -56,7 +56,7 @@ $$
 \end{aligned}
 $$
 
-Analytically solving for $x_t$ is complicated by $x_t$ occuring outside and
+Analytically solving for $x_t$ is complicated by $x_t$ occurring outside and
 inside the logit function. However, we can use
 the constraint to determine $x_t$ by numerical optimization, minimizing the
 difference between the right and the left hand side.

src/fitstats.jl

@@ -70,7 +70,7 @@ Base.eltype(::Moments{N, T}) where {N, T} = T
 
 Get the first N moments of a distribution.
 
-Procudes an object of type [`AbstractMoments`](@ref).
+Produces an object of type [`AbstractMoments`](@ref).
 
 ## Examples
 ```julia

src/univariate/continuous/estimateMoments.jl

@@ -30,7 +30,7 @@ function meanFunOfProb(d::ContinuousUnivariateDistribution;
     #---|---|---|---#
     # |---|---|---| #
     # we need to add points for δ/4 and 1-δ/4 representing the edges
-    # but their weight is only half, because they represents half an inverval
+    # but their weight is only half, because they represents half an interval
     #m = sum(c_i*δ) + el*(δ/2) + er*(δ/2) = (sum(c_i) + er/2 + el/2)*δ
     s = sum(fun.(d, p))   # sum at points c_i
     el = fun(d, δ / 4)  # 

src/univariate/continuous/logitnormal.jl

@@ -60,8 +60,8 @@ function matchModeUpper(mode::T, qp::QuantilePoint, ::Val{nTry}) where {nTry, T
     oF(mu) = ofLogitNormalModeUpper(mu, mode, logitMode, logitUpper, perc)
     ofMuTry = oF.(muTry)
     iMin = argmin(ofMuTry)
-    # on postive side muTry are increasing, on negative side muTry decreasing
-    # neet to have the lower value at the beginning of the interval
+    # on positive side muTry are increasing, on negative side muTry decreasing
+    # need to have the lower value at the beginning of the interval
     interval = (logitMode >= 0) ?
                (muTry[max(1, iMin - 1)], muTry[min(nTry, iMin + 1)]) :
                (muTry[max(1, iMin + 1)], muTry[min(nTry, max(1, iMin - 1))])
@@ -96,7 +96,7 @@ end
 
 Find the maximum-spread logitnormal distribution that has a single mode at given location.
 
-More peaked distributions with given single mode can be optained by increasing
+More peaked distributions with given single mode can be obtained by increasing
 argument peakedness. They will have a spread by originally inferred σ² devidied 
 by peakedness.