Added doc refs and tests

Author: Andy Ferris

base/linalg/conjarray.jl

@@ -3,10 +3,23 @@
 """
     ConjArray(array)
 
-A lazy-view wrapper of an `AbstractArray`, taking the elementwise complex
-conjugate. This type is usually constructed (and unwrapped) via the `conj()`
-function (or related `ctranspose()`), but currently this is the default behavior
-for `RowVector` only.
+A lazy-view wrapper of an `AbstractArray`, taking the elementwise complex conjugate. This
+type is usually constructed (and unwrapped) via the [`conj`](@ref) function (or related
+[`ctranspose`](@ref)), but currently this is the default behavior for `RowVector` only. For
+other arrays, the `ConjArray` constructor can be used directly.
+
+# Examples
+
+```jldoctest
+julia> [1+im, 1-im]'
+1×2 RowVector{Complex{Int64},ConjArray{Complex{Int64},1,Array{Complex{Int64},1}}}:
+ 1-1im  1+1im
+
+julia> ConjArray([1+im 0; 0 1-im])
+2×2 ConjArray{Complex{Int64},2,Array{Complex{Int64},2}}:
+ 1-1im  0+0im
+ 0+0im  1+1im
+```
 """
 immutable ConjArray{T, N, A <: AbstractArray} <: AbstractArray{T, N}
     parent::A

base/linalg/rowvector.jl

@@ -1,17 +1,15 @@
 """
     RowVector(vector)
 
-A lazy-view wrapper of an `AbstractVector`, which turns a length-`n` vector into
-a `1×n` shaped row vector and represents the transpose of a vector (the elements
-are also transposed recursively). This type is usually constructed (and
-unwrapped) via the `transpose()` function or `.'` operator (or related
-`ctranspose()` or `'` operator).
-
-By convention, a vector can be multiplied by a matrix on its left (`A * v`)
-whereas a row vector can be multiplied by a matrix on its right (such that
-`v.' * A = (A.' * v).'`). It differs from a `1×n`-sized matrix by the facts that
-its transpose returns a vector and the inner product `v1.' * v2` returns a
-scalar, but will otherwise behave similarly.
+A lazy-view wrapper of an `AbstractVector`, which turns a length-`n` vector into a `1×n`
+shaped row vector and represents the transpose of a vector (the elements are also transposed
+recursively). This type is usually constructed (and unwrapped) via the [`transpose`](@ref)
+function or `.'` operator (or related [`ctranspose`](@ref) or `'` operator).
+
+By convention, a vector can be multiplied by a matrix on its left (`A * v`) whereas a row
+vector can be multiplied by a matrix on its right (such that `v.' * A = (A.' * v).'`). It
+differs from a `1×n`-sized matrix by the facts that its transpose returns a vector and the
+inner product `v1.' * v2` returns a scalar, but will otherwise behave similarly.
 """
 immutable RowVector{T,V<:AbstractVector} <: AbstractMatrix{T}
     vec::V
@@ -89,7 +87,19 @@ parent(rowvec::RowVector) = rowvec.vec
 """
     conj(rowvector)
 
-Returns a `ConjArray` lazy view of the input, where each element is conjugated.
+Returns a [`ConjArray`](@ref) lazy view of the input, where each element is conjugated.
+
+### Example
+
+```jldoctest
+julia> v = [1+im, 1-im].'
+1×2 RowVector{Complex{Int64},Array{Complex{Int64},1}}:
+ 1+1im  1-1im
+
+julia> conj(v)
+1×2 RowVector{Complex{Int64},ConjArray{Complex{Int64},1,Array{Complex{Int64},1}}}:
+ 1-1im  1+1im
+```
 """
 @inline conj(rowvec::RowVector) = RowVector(_conj(rowvec.vec))
 @inline conj{T<:Real}(rowvec::RowVector{T}) = rowvec