Improve docstrings

Author: juliohm

src/transforms.jl

@@ -99,7 +99,7 @@ these functions in parallel for all columns with multiple threads.
 
 ## Notes
 
-* All ColwiseFeatureTransform subtypes must have a `colspec` field.
+* `ColwiseFeatureTransform` subtypes must have a `colspec` field.
 """
 abstract type ColwiseFeatureTransform <: FeatureTransform end
 

src/transforms/coalesce.jl

@@ -5,7 +5,7 @@
 """
     Coalesce(; value)
 
-Replaces all missing values from the table with `value`.
+Replaces all `missing` values from the table with `value`.
 
     Coalesce(col₁, col₂, ..., colₙ; value)
     Coalesce([col₁, col₂, ..., colₙ]; value)

src/transforms/eigenanalysis.jl

@@ -3,12 +3,11 @@
 # ------------------------------------------------------------------
 
 """
-    EigenAnalysis(proj; maxdim=nothing, pratio=1.0)
+    EigenAnalysis(proj; [maxdim], [pratio])
 
 The eigenanalysis of the covariance with a given projection `proj`.
-Optionally specify the maximum number of dimensions in the output `maxdim`
-and the percentage of variance to retain `pratio`. Default to all dimensions of
-the input.
+Optionally specify the maximum number of dimensions in the output
+`maxdim` and the percentage of variance to retain `pratio`.
 
 ## Projections
 
@@ -161,12 +160,12 @@ function eigenmatrices(transform, Y)
 end
 
 """
-    PCA(; maxdim=nothing, pratio=1.0)
+    PCA([options])
 
-The PCA transform is a shortcut for
-`ZScore() → EigenAnalysis(:V; maxdim, pratio)`.
+Principal component analysis.
 
-See also: [`ZScore`](@ref), [`EigenAnalysis`](@ref).
+See [`EigenAnalysis`](@ref) for detailed
+description of the available options.
 
 # Examples
 
@@ -175,16 +174,20 @@ PCA(maxdim=2)
 PCA(pratio=0.86)
 PCA(maxdim=2, pratio=0.86)
 ```
+
+## Notes
+
+* `PCA()` is shortcut for `ZScore() → EigenAnalysis(:V)`.
 """
 PCA(; maxdim=nothing, pratio=1.0) = ZScore() → EigenAnalysis(:V, maxdim, pratio)
 
 """
-    DRS(; maxdim=nothing, pratio=1.0)
+    DRS([options])
 
-The DRS transform is a shortcut for
-`ZScore() → EigenAnalysis(:VD; maxdim, pratio)`.
+Dimension reduction sphering.
 
-See also: [`ZScore`](@ref), [`EigenAnalysis`](@ref).
+See [`EigenAnalysis`](@ref) for detailed
+description of the available options.
 
 # Examples
 
@@ -193,16 +196,20 @@ DRS(maxdim=3)
 DRS(pratio=0.87)
 DRS(maxdim=3, pratio=0.87)
 ```
+
+## Notes
+
+* `DRS()` is shortcut for `ZScore() → EigenAnalysis(:VD)`.
 """
 DRS(; maxdim=nothing, pratio=1.0) = ZScore() → EigenAnalysis(:VD, maxdim, pratio)
 
 """
-    SDS(; maxdim=nothing, pratio=1.0)
+    SDS([options])
 
-The SDS transform is a shortcut for
-`ZScore() → EigenAnalysis(:VDV; maxdim, pratio)`.
+Standardized data sphering.
 
-See also: [`ZScore`](@ref), [`EigenAnalysis`](@ref).
+See [`EigenAnalysis`](@ref) for detailed
+description of the available options.
 
 # Examples
 
@@ -212,5 +219,9 @@ SDS(maxdim=4)
 SDS(pratio=0.88)
 SDS(maxdim=4, pratio=0.88)
 ```
+
+## Notes
+
+* `SDS()` is shortcut for `ZScore() → EigenAnalysis(:VDV)`.
 """
 SDS(; maxdim=nothing, pratio=1.0) = ZScore() → EigenAnalysis(:VDV, maxdim, pratio)

src/transforms/filter.jl

@@ -102,7 +102,7 @@ DropMissing(r"[bce]")
 ## Notes
 
 * The transform can alter the element type of columns from `Union{Missing,T}` to `T`.
-* If the transformed column has only missing values, it will be converted to an empty column of type `Any`.
+* If the transformed column has only `missing` values, it will be converted to an empty column of type `Any`.
 """
 struct DropMissing{S<:ColSpec} <: StatelessFeatureTransform
   colspec::S

src/transforms/parallel.jl

@@ -18,10 +18,9 @@ Scale(low=0.3, high=0.6) ⊔ EigenAnalysis(:VDV)
 ZScore() ⊔ EigenAnalysis(:V)
 ```
 
-### Notes
+## Notes
 
-- Metadata is transformed with the first revertible
-  transform in the list of `transforms`.
+* Metadata is transformed with the first revertible transform in the list of `transforms`.
 """
 struct ParallelTableTransform <: TableTransform
   transforms::Vector{Transform}

src/transforms/scale.jl

@@ -30,10 +30,6 @@ Scale([:a, :c, :e], low=0.3, high=0.7)
 Scale(("a", "c", "e"), low=0.25, high=0.75)
 Scale(r"[ace]", low=0.3, high=0.7)
 ```
-
-## Notes
-
-* The `low` and `high` values are restricted to the interval [0, 1].
 """
 struct Scale{S<:ColSpec,T} <: ColwiseFeatureTransform
   colspec::S