diff --git a/src/algorithms/nncluster.jl b/src/algorithms/nncluster.jl
index 0a0c519..a52af29 100644
--- a/src/algorithms/nncluster.jl
+++ b/src/algorithms/nncluster.jl
@@ -2,7 +2,7 @@
     NearestNeighborCluster <: NeutralLandscapeMaker
 
     NearestNeighborCluster(; p=0.5, n=:rook)
-    NearestNeighborCluster(p, n)
+    NearestNeighborCluster(p, [n=:rook])
 
 Create a random cluster nearest-neighbour neutral landscape model with 
 values ranging 0-1. `p` sets the density of original clusters, and `n`
@@ -11,7 +11,7 @@ sets the neighborhood for clustering (see `?label` for neighborhood options)
 @kwdef struct NearestNeighborCluster <: NeutralLandscapeMaker
     p::Float64 = 0.5
     n::Symbol = :rook
-    function NearestNeighborCluster(p::Float64, n::Symbol) 
+    function NearestNeighborCluster(p::Float64, n::Symbol = :rook) 
         @assert p > 0
         @assert n ∈ (:rook, :queen, :diagonal)
         new(p,n)
diff --git a/src/algorithms/nnelement.jl b/src/algorithms/nnelement.jl
index 2c96b98..a5ba8cf 100644
--- a/src/algorithms/nnelement.jl
+++ b/src/algorithms/nnelement.jl
@@ -2,7 +2,7 @@
     NearestNeighborElement <: NeutralLandscapeMaker
 
     NearestNeighborElement(; n=3, k=1)
-    NearestNeighborElement(n, k)
+    NearestNeighborElement(n, [k=1])
 
 Assigns a value to each patch using a k-NN algorithmm with `n` initial clusters
 and `k` neighbors. The default is to use three cluster and a single neighbor.
diff --git a/src/algorithms/perlinnoise.jl b/src/algorithms/perlinnoise.jl
index 8af04f8..3265682 100644
--- a/src/algorithms/perlinnoise.jl
+++ b/src/algorithms/perlinnoise.jl
@@ -2,7 +2,7 @@
     PerlinNoise <: NeutralLandscapeMaker
 
     PerlinNoise(; kw...)
-    PerlinNoise(periods, octaves, lacunarity, persistance, valley)
+    PerlinNoise(periods, [octaves=1, lacunarity=2, persistance=0.5, valley=:u])
 
 Create a Perlin noise neutral landscape model with values ranging 0-1.
 
@@ -13,7 +13,7 @@ Create a Perlin noise neutral landscape model with values ranging 0-1.
 - `octaves::Int=1`: the number of octaves that will form the Perlin noise.
 - `lacunarity::Int=2` : the rate at which the frequency of periods increases for each 
     octive.
-- `persistance::Real=0.5` : the rate at which the amplitude of periods decreases for each 
+- `persistance::Float64=0.5` : the rate at which the amplitude of periods decreases for each 
     octive.
 - `valley::Symbol=`:u`: the kind of valley bottom that will be mimicked: `:u` produces 
     u-shaped valleys, `:v` produces v-shaped valleys, and `:-` produces flat bottomed 
@@ -23,12 +23,15 @@ Note: This is a memory-intensive algorithm with some settings. Be careful using
 prime numbers for `period` when also using a large array size, high lacuarity and/or many 
 octaves. Memory use scales with the lowest common multiple of `periods`.
 """
-@kwdef struct PerlinNoise{PT<:Real} <: NeutralLandscapeMaker 
+@kwdef struct PerlinNoise <: NeutralLandscapeMaker 
     periods::Tuple{Int,Int} = (1, 1)
     octaves::Int = 1
     lacunarity::Int = 2
-    persistance::PT = 0.5
+    persistance::Float64 = 0.5
     valley::Symbol = :u
+    function PerlinNoise(periods, octaves=1, lacunarity=2, persistance=0.5, valley=:u)
+        new(periods, octaves, lacunarity, persistance, valley)
+    end
 end
 
 function _landscape!(A, alg::PerlinNoise)
diff --git a/src/algorithms/rectangularcluster.jl b/src/algorithms/rectangularcluster.jl
index a1443a6..f2b6ff6 100644
--- a/src/algorithms/rectangularcluster.jl
+++ b/src/algorithms/rectangularcluster.jl
@@ -2,7 +2,7 @@
     RectangularCluster <: NeutralLandscapeMaker
 
     RectangularCluster(; minimum=2, maximum=4)
-    RectangularCluster(minimum, maximum)
+    RectangularCluster(minimum, [maximum=4])
 
 Fills the landscape with rectangles containing a random value. The size of each
 rectangle/patch is between `minimum` and `maximum` (the two can be equal for a
@@ -11,9 +11,9 @@ fixed size rectangle).
 @kwdef struct RectangularCluster <: NeutralLandscapeMaker
     minimum::Integer = 2
     maximum::Integer = 4
-    function RectangularCluster(x::T, y::T) where {T <: Integer}
-        @assert 0 < x <= y
-        new(x, y)
+    function RectangularCluster(minimum::T, maximum::T=4) where {T <: Integer}
+        @assert 0 < minimum <= maximum
+        new(minimum, maximum)
     end
 end
 
diff --git a/src/algorithms/wavesurface.jl b/src/algorithms/wavesurface.jl
index af43347..fa86d82 100644
--- a/src/algorithms/wavesurface.jl
+++ b/src/algorithms/wavesurface.jl
@@ -2,7 +2,7 @@
     WaveSurface <: NeutralLandscapeMaker
 
     WaveSurface(; direction=360rand(), periods=1)
-    WaveSurface(direction, periods)
+    WaveSurface(direction, [periods=1])
 
 Creates a sinusoidal landscape with a `direction` and a number of `periods`. If
 neither are specified, there will be a single period of random direction.
@@ -10,9 +10,9 @@ neither are specified, there will be a single period of random direction.
 @kwdef struct WaveSurface <: NeutralLandscapeMaker
     direction::Float64 = 360rand()
     periods::Int64 = 1
-    function WaveSurface(x::T, y::K) where {T <: Real, K <: Integer}
-        @assert y >= 1
-        new(mod(x, 360.0), y)
+    function WaveSurface(direction::T, periods::K = 1) where {T <: Real, K <: Integer}
+        @assert periods >= 1
+        new(mod(direction, 360.0), periods)
     end
 end
 
diff --git a/test/rand.jl b/test/rand.jl
index 5fa675a..2b08295 100644
--- a/test/rand.jl
+++ b/test/rand.jl
@@ -2,14 +2,39 @@ using NeutralLandscapes, Test
 
 algorithms = (
    DiamondSquare(),
+   DiamondSquare(; H=0.2),
+   DiamondSquare(0.4),
+   DiscreteVoronoi(),
+   DiscreteVoronoi(; n=2),
+   DiscreteVoronoi(3),
+   DistanceGradient(),
+   DistanceGradient(; sources=[2]),
+   DistanceGradient([1, 2]),
    EdgeGradient(),
+   EdgeGradient(; direction=120.0),
+   EdgeGradient(90.0),
    MidpointDisplacement(),
+   MidpointDisplacement(;),
    NearestNeighborElement(),
+   NearestNeighborElement(; k=1),
+   NearestNeighborElement(2),
+   NearestNeighborCluster(),
+   # NearestNeighborCluster(; n=:queen),
+   NearestNeighborCluster(; n=:diagonal),
+   NearestNeighborCluster(0.2, :diagonal),
    NoGradient(),
    PerlinNoise(),
+   PerlinNoise(; octaves=3, lacunarity=3, persistance=0.3),
+   PerlinNoise((1, 2), 3, 2, 0.2),
    PlanarGradient(),
+   PlanarGradient(; direction=120.0),
+   PlanarGradient(90),
    RectangularCluster(),
+   RectangularCluster(; maximum=5),
+   RectangularCluster(2),
    WaveSurface(), 
+   WaveSurface(90.0), 
+   WaveSurface(; periods=2), 
 )
 
 sizes = (50, 200), (100, 100), (301, 87)