diff --git a/test/empirical.jl b/test/empirical.jl
index 620989a..c639540 100644
--- a/test/empirical.jl
+++ b/test/empirical.jl
@@ -1,153 +1,151 @@
-@testset "Empirical" begin
-  @testset "Variogram" begin
-    # homogeneous field has zero variogram
-    sdata = georef((z=ones(3),), [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)])
-    g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2.0)
-    @test g.abscissas ≈ [1 / 2, √2] * u"m"
-    @test g.ordinates[2] == 0.0
-    @test g.counts == [0, 3]
+@testset "EmpiricalVariogram" begin
+  # homogeneous field has zero variogram
+  sdata = georef((z=ones(3),), [(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)])
+  g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2.0)
+  @test g.abscissas ≈ [1 / 2, √2] * u"m"
+  @test g.ordinates[2] == 0.0
+  @test g.counts == [0, 3]
 
-    # basic test on number of lags
-    sdata = georef((z=[1.0, 0.0, 1.0],), [(25.0, 25.0), (50.0, 75.0), (75.0, 50.0)])
-    g = EmpiricalVariogram(sdata, :z, nlags=20, maxlag=1.0)
-    @test length(g.abscissas) == 20
-    @test length(g.ordinates) == 20
-    @test length(g.counts) == 20
+  # basic test on number of lags
+  sdata = georef((z=[1.0, 0.0, 1.0],), [(25.0, 25.0), (50.0, 75.0), (75.0, 50.0)])
+  g = EmpiricalVariogram(sdata, :z, nlags=20, maxlag=1.0)
+  @test length(g.abscissas) == 20
+  @test length(g.ordinates) == 20
+  @test length(g.counts) == 20
 
-    # empirical variogram on integer coordinates
-    sdata = georef((z=ones(3),), [(1, 0, 0), (0, 1, 0), (0, 0, 1)])
-    g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2, algorithm=:full)
-    @test g.abscissas ≈ [1 / 2, √2] * u"m"
-    @test g.ordinates[2] == 0.0
-    @test g.counts == [0, 3]
+  # empirical variogram on integer coordinates
+  sdata = georef((z=ones(3),), [(1, 0, 0), (0, 1, 0), (0, 0, 1)])
+  g = EmpiricalVariogram(sdata, :z, nlags=2, maxlag=2, algorithm=:full)
+  @test g.abscissas ≈ [1 / 2, √2] * u"m"
+  @test g.ordinates[2] == 0.0
+  @test g.counts == [0, 3]
 
-    # empirical variogram with only missing data
-    z = Union{Float64,Missing}[missing, missing, missing]
-    𝒟 = georef((z=z,), rand(Point, 3))
-    g = EmpiricalVariogram(𝒟, :z, maxlag=1.0, nlags=5)
-    @test g.abscissas == [0.1, 0.3, 0.5, 0.7, 0.9] * u"m"
-    @test all(iszero, g.counts)
+  # empirical variogram with only missing data
+  z = Union{Float64,Missing}[missing, missing, missing]
+  𝒟 = georef((z=z,), rand(Point, 3))
+  g = EmpiricalVariogram(𝒟, :z, maxlag=1.0, nlags=5)
+  @test g.abscissas == [0.1, 0.3, 0.5, 0.7, 0.9] * u"m"
+  @test all(iszero, g.counts)
 
-    # accumulation algorithms give the same result
-    rng = StableRNG(123)
-    sdata = georef((z=rand(rng, 1000),), rand(rng, Point, 1000))
-    g₁ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:full)
-    g₂ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:ball)
-    @test isequal(g₁.abscissas, g₂.abscissas)
-    @test isequal(g₁.ordinates, g₂.ordinates)
-    @test isequal(g₁.counts, g₂.counts)
+  # accumulation algorithms give the same result
+  rng = StableRNG(123)
+  sdata = georef((z=rand(rng, 1000),), rand(rng, Point, 1000))
+  g₁ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:full)
+  g₂ = EmpiricalVariogram(sdata, :z, maxlag=0.01, algorithm=:ball)
+  @test isequal(g₁.abscissas, g₂.abscissas)
+  @test isequal(g₁.ordinates, g₂.ordinates)
+  @test isequal(g₁.counts, g₂.counts)
 
-    # custom distance is recorded
-    rng = StableRNG(123)
-    sdata = georef((z=rand(rng, 2),), [Point(LatLon(0.0, 0.0)), Point(LatLon(0.0, 90.0))])
-    g = EmpiricalVariogram(sdata, :z, distance=Haversine(6371.0), algorithm=:full)
-    @test g.distance == Haversine(6371.0)
+  # custom distance is recorded
+  rng = StableRNG(123)
+  sdata = georef((z=rand(rng, 2),), [Point(LatLon(0.0, 0.0)), Point(LatLon(0.0, 90.0))])
+  g = EmpiricalVariogram(sdata, :z, distance=Haversine(6371.0), algorithm=:full)
+  @test g.distance == Haversine(6371.0)
 
-    # print methods
-    rng = StableRNG(123)
-    d = georef((z=rand(rng, 100, 100),))
-    g = EmpiricalVariogram(d, :z)
-    @test sprint(show, g) ==
-          "EmpiricalVariogram(abscissas: [0.25 m, ..., 9.93304 m], ordinates: [0.0, ..., 0.0841979], distance: Euclidean(0.0), estimator: MatheronEstimator(), npairs: 1447200)"
-    @test sprint(show, MIME"text/plain"(), g) == """
-    EmpiricalVariogram
-    ├─ abscissas: [0.25 m, 1.0 m, 1.41421 m, ..., 8.7407 m, 9.28182 m, 9.93304 m]
-    ├─ ordinates: [0.0, 0.0843099, 0.0845995, ..., 0.0838336, 0.0839823, 0.0841979]
-    ├─ distance: Euclidean(0.0)
-    ├─ estimator: MatheronEstimator()
-    └─ npairs: 1447200"""
+  # print methods
+  rng = StableRNG(123)
+  d = georef((z=rand(rng, 100, 100),))
+  g = EmpiricalVariogram(d, :z)
+  @test sprint(show, g) ==
+        "EmpiricalVariogram(abscissas: [0.25 m, ..., 9.93304 m], ordinates: [0.0, ..., 0.0841979], distance: Euclidean(0.0), estimator: MatheronEstimator(), npairs: 1447200)"
+  @test sprint(show, MIME"text/plain"(), g) == """
+  EmpiricalVariogram
+  ├─ abscissas: [0.25 m, 1.0 m, 1.41421 m, ..., 8.7407 m, 9.28182 m, 9.93304 m]
+  ├─ ordinates: [0.0, 0.0843099, 0.0845995, ..., 0.0838336, 0.0839823, 0.0841979]
+  ├─ distance: Euclidean(0.0)
+  ├─ estimator: MatheronEstimator()
+  └─ npairs: 1447200"""
 
-    # test variography with compositional data
-    data = georef((z=rand(Composition{3}, 100),), rand(Point, 100))
-    g = EmpiricalVariogram(data, :z, maxlag=1.0, algorithm=:full)
-    @test all(≥(0u"m"), g.abscissas)
-    @test all(≥(0), g.ordinates)
-    @test all(≥(0), g.counts)
+  # test variography with compositional data
+  data = georef((z=rand(Composition{3}, 100),), rand(Point, 100))
+  g = EmpiricalVariogram(data, :z, maxlag=1.0, algorithm=:full)
+  @test all(≥(0u"m"), g.abscissas)
+  @test all(≥(0), g.ordinates)
+  @test all(≥(0), g.counts)
 
-    # test variography with unitful data
-    data = georef((z=[1 * u"K" for i in 1:100],), rand(Point, 100))
-    g = EmpiricalVariogram(data, :z, nlags=20)
-    @test all(≥(0u"m"), g.abscissas)
-    @test g.ordinates == fill(0.0 * u"K^2", 20)
+  # test variography with unitful data
+  data = georef((z=[1 * u"K" for i in 1:100],), rand(Point, 100))
+  g = EmpiricalVariogram(data, :z, nlags=20)
+  @test all(≥(0u"m"), g.abscissas)
+  @test g.ordinates == fill(0.0 * u"K^2", 20)
 
-    # Matheron's vs Cressie's estimator
-    img = readdlm(joinpath(datadir, "Gaussian30x10.txt"))
-    data = georef((; Z=img))
-    g₁ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:matheron)
-    g₂ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:cressie)
-    @test g₁.abscissas == g₂.abscissas
-    @test all(isapprox.(g₁.ordinates, g₂.ordinates, atol=0.1))
-    @test g₁.counts == g₂.counts
+  # Matheron's vs Cressie's estimator
+  img = readdlm(joinpath(datadir, "Gaussian30x10.txt"))
+  data = georef((; Z=img))
+  g₁ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:matheron)
+  g₂ = EmpiricalVariogram(data, :Z, maxlag=50.0, estimator=:cressie)
+  @test g₁.abscissas == g₂.abscissas
+  @test all(isapprox.(g₁.ordinates, g₂.ordinates, atol=0.1))
+  @test g₁.counts == g₂.counts
 
-    # specify variables as strings
-    img = readdlm(joinpath(datadir, "Gaussian30x10.txt"))
-    data = georef((; Z=img))
-    g = EmpiricalVariogram(data, "Z", maxlag=50.0)
-    @test all(≥(0u"m"), g.abscissas)
-    @test all(>(0.8), g.ordinates[11:end])
-    @test all(≥(0), g.counts)
-  end
+  # specify variables as strings
+  img = readdlm(joinpath(datadir, "Gaussian30x10.txt"))
+  data = georef((; Z=img))
+  g = EmpiricalVariogram(data, "Z", maxlag=50.0)
+  @test all(≥(0u"m"), g.abscissas)
+  @test all(>(0.8), g.ordinates[11:end])
+  @test all(≥(0), g.counts)
+end
 
-  @testset "Varioplane" begin
-    img = readdlm(joinpath(datadir, "anisotropic.tsv"))
-    data = georef((z=img,))
-    g = EmpiricalVarioplane(data, :z, maxlag=50.0)
-    @test sprint(show, g) == "EmpiricalVarioplane"
-    @test sprint(show, MIME"text/plain"(), g) == """
-    EmpiricalVarioplane
-      50 angles
-      └─0.00°
-      └─3.67°
-      └─7.35°
-      └─11.02°
-      └─14.69°
-      ⋮
-      └─165.31°
-      └─168.98°
-      └─172.65°
-      └─176.33°
-      └─180.00°"""
-  end
+@testset "DirectionalVariogram" begin
+  # merge operation does not produce NaN
+  dir = (0.286788, -0.496732, -0.819152)
+  𝒟 = georef(CSV.File(joinpath(datadir, "nanlags.csv")), (:X, :Y, :Z))
+  g = DirectionalVariogram(dir, 𝒟, :Cu, dtol=45, maxlag=150, nlags=20)
+  @test !any(isnan.(g.abscissas))
+  @test !any(isnan.(g.ordinates))
+  @test !any(isnan.(g.counts))
 
-  @testset "Directional" begin
-    # merge operation does not produce NaN
-    dir = (0.286788, -0.496732, -0.819152)
-    𝒟 = georef(CSV.File(joinpath(datadir, "nanlags.csv")), (:X, :Y, :Z))
-    g = DirectionalVariogram(dir, 𝒟, :Cu, dtol=45, maxlag=150, nlags=20)
-    @test !any(isnan.(g.abscissas))
-    @test !any(isnan.(g.ordinates))
-    @test !any(isnan.(g.counts))
+  # directional variogram and known anisotropy ratio
+  img = readdlm(joinpath(datadir, "anisotropic.tsv"))
+  sdata = georef((z=img,))
+  gₕ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
+  gᵥ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
+  γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ)
+  γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ)
+  @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1
+end
 
-    # directional variogram and known anisotropy ratio
-    img = readdlm(joinpath(datadir, "anisotropic.tsv"))
-    sdata = georef((z=img,))
-    gₕ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
-    gᵥ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
-    γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ)
-    γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ)
-    @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1
-  end
+@testset "PlanarVariogram" begin
+  # directional equals planar rotated by 90 degrees in 2D
+  img = readdlm(joinpath(datadir, "anisotropic.tsv"))
+  sdata = georef((z=img,))
+  g₁ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
+  g₂ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
+  @test g₁.abscissas == g₂.abscissas
+  @test g₁.ordinates ≈ g₂.ordinates
+  @test g₁.counts == g₂.counts
+  g₁ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
+  g₂ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
+  @test g₁.abscissas == g₂.abscissas
+  @test g₁.ordinates ≈ g₂.ordinates
+  @test g₁.counts == g₂.counts
 
-  @testset "Planar" begin
-    # directional equals planar rotated by 90 degrees in 2D
-    img = readdlm(joinpath(datadir, "anisotropic.tsv"))
-    sdata = georef((z=img,))
-    g₁ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
-    g₂ = DirectionalVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
-    @test g₁.abscissas == g₂.abscissas
-    @test g₁.ordinates ≈ g₂.ordinates
-    @test g₁.counts == g₂.counts
-    g₁ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
-    g₂ = DirectionalVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
-    @test g₁.abscissas == g₂.abscissas
-    @test g₁.ordinates ≈ g₂.ordinates
-    @test g₁.counts == g₂.counts
+  # planar variogram and known anisotropy ratio
+  gₕ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
+  gᵥ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
+  γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ)
+  γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ)
+  @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1
+end
 
-    # planar variogram and known anisotropy ratio
-    gₕ = PlanarVariogram((0.0, 1.0), sdata, :z, maxlag=50.0)
-    gᵥ = PlanarVariogram((1.0, 0.0), sdata, :z, maxlag=50.0)
-    γₕ = GeoStatsFunctions.fit(GaussianVariogram, gₕ)
-    γᵥ = GeoStatsFunctions.fit(GaussianVariogram, gᵥ)
-    @test range(γₕ) / range(γᵥ) ≈ 3.0 atol = 0.1
-  end
+@testset "EmpiricalVarioplane" begin
+  img = readdlm(joinpath(datadir, "anisotropic.tsv"))
+  data = georef((z=img,))
+  g = EmpiricalVarioplane(data, :z, maxlag=50.0)
+  @test sprint(show, g) == "EmpiricalVarioplane"
+  @test sprint(show, MIME"text/plain"(), g) == """
+  EmpiricalVarioplane
+    50 angles
+    └─0.00°
+    └─3.67°
+    └─7.35°
+    └─11.02°
+    └─14.69°
+    ⋮
+    └─165.31°
+    └─168.98°
+    └─172.65°
+    └─176.33°
+    └─180.00°"""
 end
diff --git a/test/transiogram.jl b/test/transiogram.jl
index bc6ed5e..0e4f60b 100644
--- a/test/transiogram.jl
+++ b/test/transiogram.jl
@@ -1,4 +1,4 @@
-@testset "Transiogram" begin
+@testset "ExponentialTransiogram" begin
   # base transition rate matrix
   R = GeoStatsFunctions.baseratematrix([1.0, 2.0, 3.0]u"m", [0.2, 0.5, 0.3])
   @test R ==