diff --git a/.buildkite/pipeline.yml b/.buildkite/pipeline.yml
index c29c1a0b7..c61d02151 100644
--- a/.buildkite/pipeline.yml
+++ b/.buildkite/pipeline.yml
@@ -52,8 +52,8 @@ steps:
       export PYTHONHOME="$$JULIA_DEPOT_PATH/conda/3/bin"
       export CONDA_JL_HOME="$$JULIA_DEPOT_PATH/conda/3"
 
-      mkdir examples/GaussianProcessEmulator/depot
-      export JULIA_DEPOT_PATH="$$(pwd)/examples/GaussianProcessEmulator/depot:$JULIA_DEPOT_PATH"
+      mkdir examples/Emulator/GaussianProcess/depot
+      export JULIA_DEPOT_PATH="$$(pwd)/examples/Emulator/GaussianProcess/depot:$JULIA_DEPOT_PATH"
 
       julia --color=yes --project -e '
         println("--- Instantiating Project")
@@ -61,12 +61,10 @@ steps:
         Pkg.instantiate()
         Pkg.activate("examples/GaussianProcessEmulator")
         Pkg.instantiate()
-        println("+++ Running Learn Noise")
-        include("examples/GaussianProcessEmulator/learn_noise.jl")
         println("+++ Running PlotGP")
-        include("examples/GaussianProcessEmulator/plot_GP.jl")'
+        include("examples/Emulator/GaussianProcess/plot_GP.jl")'
     artifact_paths:
-      - "examples/GaussianProcessEmulator/output/*.png"
+      - "examples/Emulator/GaussianProcess/output/*.png"
     env:
       PYTHON: "$$JULIA_DEPOT_PATH/conda/3/bin/python"
       PYTHONHOME: "$$JULIA_DEPOT_PATH/conda/3/bin"
diff --git a/.github/workflows/Docs.yml b/.github/workflows/Docs.yml
index 4b7afa04f..794b7cdd9 100644
--- a/.github/workflows/Docs.yml
+++ b/.github/workflows/Docs.yml
@@ -26,7 +26,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
         with:
-          version: 1.5.4
+          version: 1.6
       - name: Cache artifacts
         uses: actions/cache@v1
         env:
diff --git a/.github/workflows/Tests.yml b/.github/workflows/Tests.yml
index 9939624e2..e2abf00d2 100644
--- a/.github/workflows/Tests.yml
+++ b/.github/workflows/Tests.yml
@@ -26,7 +26,7 @@ jobs:
     - name: Set up Julia
       uses: julia-actions/setup-julia@v1
       with:
-        version: 1.5.4
+        version: 1.6
  
     - name: Install Julia Project Packages
       # we add this ENV varaible to force PyCall to download and use Conda rather than
@@ -80,7 +80,7 @@ jobs:
     - name: Set up Julia
       uses: julia-actions/setup-julia@v1
       with:
-        version: 1.5.4
+        version: 1.6
  
     - name: Install Julia Project Packages
       env:
@@ -119,7 +119,7 @@ jobs:
     - name: Set up Julia
       uses: julia-actions/setup-julia@v1
       with:
-        version: 1.5.4
+        version: 1.6
  
     - name: Install Julia Project Packages
       env:
diff --git a/Manifest.toml b/Manifest.toml
index 0ed911242..c78a0570d 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,55 +1,78 @@
 # This file is machine-generated - editing it directly is not advised
 
-[[AbstractFFTs]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716"
-uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
-version = "0.5.0"
+[[AMD]]
+deps = ["Libdl", "LinearAlgebra", "SparseArrays", "Test"]
+git-tree-sha1 = "fc66ffc5cff568936649445f58a55b81eaf9592c"
+uuid = "14f7f29c-3bd6-536c-9a0b-7339e30b5a3e"
+version = "0.4.0"
+
+[[AbstractTrees]]
+git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5"
+uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
+version = "0.3.4"
 
 [[Adapt]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "ffcfa2d345aaee0ef3d8346a073d5dd03c983ebe"
+git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7"
 uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "3.2.0"
+version = "3.3.1"
+
+[[ArgTools]]
+uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
 
 [[ArrayInterface]]
-deps = ["IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"]
-git-tree-sha1 = "da557609446152beb6ee134683d7b79ece129eae"
+deps = ["Compat", "IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"]
+git-tree-sha1 = "265b06e2b1f6a216e0e8f183d28e4d354eab3220"
 uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
-version = "3.1.5"
-
-[[ArrayLayouts]]
-deps = ["Compat", "FillArrays", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "a577e27915fdcb3f6b96118b56655b38e3b466f2"
-uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
-version = "0.4.12"
+version = "3.2.1"
 
 [[Artifacts]]
-deps = ["Pkg"]
-git-tree-sha1 = "c30985d8821e0cd73870b17b0ed0ce6dc44cb744"
 uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
-version = "1.3.0"
 
 [[Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 
-[[CategoricalArrays]]
-deps = ["DataAPI", "Future", "JSON", "Missings", "Printf", "Statistics", "StructTypes", "Unicode"]
-git-tree-sha1 = "dbfddfafb75fae5356e00529ce67454125935945"
-uuid = "324d7699-5711-5eae-9e2f-1d82baa6b597"
-version = "0.9.3"
+[[BenchmarkTools]]
+deps = ["JSON", "Logging", "Printf", "Profile", "Statistics", "UUIDs"]
+git-tree-sha1 = "940001114a0147b6e4d10624276d56d531dd9b49"
+uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+version = "1.2.2"
+
+[[BinaryProvider]]
+deps = ["Libdl", "Logging", "SHA"]
+git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058"
+uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
+version = "0.5.10"
 
-[[ChainRules]]
-deps = ["ChainRulesCore", "Compat", "LinearAlgebra", "Random", "Reexport", "Requires", "Statistics"]
-git-tree-sha1 = "e01f521443e3700f40ad3c7c1c6aa3a6940aaea1"
-uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2"
-version = "0.7.54"
+[[Bzip2_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
+uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
+version = "1.0.8+0"
 
 [[ChainRulesCore]]
 deps = ["Compat", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "de4f08843c332d355852721adb1592bce7924da3"
+git-tree-sha1 = "4c26b4e9e91ca528ea212927326ece5918a04b47"
 uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "0.9.29"
+version = "1.11.2"
+
+[[ChangesOfVariables]]
+deps = ["ChainRulesCore", "LinearAlgebra", "Test"]
+git-tree-sha1 = "bf98fa45a0a4cee295de98d4c1462be26345b9a1"
+uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0"
+version = "0.1.2"
+
+[[CodecBzip2]]
+deps = ["Bzip2_jll", "Libdl", "TranscodingStreams"]
+git-tree-sha1 = "2e62a725210ce3c3c2e1a3080190e7ca491f18d7"
+uuid = "523fee87-0ab8-5b00-afb7-3ecf72e48cfd"
+version = "0.7.2"
+
+[[CodecZlib]]
+deps = ["TranscodingStreams", "Zlib_jll"]
+git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
+uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
+version = "0.7.0"
 
 [[CommonSubexpressions]]
 deps = ["MacroTools", "Test"]
@@ -59,27 +82,25 @@ version = "0.3.0"
 
 [[Compat]]
 deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
-git-tree-sha1 = "919c7f3151e79ff196add81d7f4e45d91bbf420b"
+git-tree-sha1 = "44c37b4636bc54afac5c574d2d02b625349d6582"
 uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "3.25.0"
+version = "3.41.0"
 
 [[CompilerSupportLibraries_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "8e695f735fca77e9708e795eda62afdb869cbb70"
+deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "0.3.4+0"
 
 [[Conda]]
-deps = ["JSON", "VersionParsing"]
-git-tree-sha1 = "c0647249d785f1d5139c0cc96db8f6b32f7ec416"
+deps = ["Downloads", "JSON", "VersionParsing"]
+git-tree-sha1 = "6cdc8832ba11c7695f494c9d9a1c31e90959ce0f"
 uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
-version = "1.5.0"
+version = "1.6.0"
 
-[[CpuId]]
-deps = ["Markdown", "Test"]
-git-tree-sha1 = "f0464e499ab9973b43c20f8216d088b61fda80c6"
-uuid = "adafc99b-e345-5852-983c-f28acb93d879"
-version = "0.2.2"
+[[Convex]]
+deps = ["AbstractTrees", "BenchmarkTools", "LDLFactorizations", "LinearAlgebra", "MathOptInterface", "OrderedCollections", "SparseArrays", "Test"]
+git-tree-sha1 = "145c5e0b3ea3c9dd3bba134a58bab4112aa250c8"
+uuid = "f65535da-76fb-5f13-bab9-19810c17039a"
+version = "0.14.18"
 
 [[Crayons]]
 git-tree-sha1 = "3f71217b538d7aaee0b69ab47d9b7724ca8afa0d"
@@ -87,21 +108,21 @@ uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
 version = "4.0.4"
 
 [[DataAPI]]
-git-tree-sha1 = "dfb3b7e89e395be1e25c2ad6d7690dc29cc53b1d"
+git-tree-sha1 = "cc70b17275652eb47bc9e5f81635981f13cea5c8"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.6.0"
+version = "1.9.0"
 
 [[DataFrames]]
-deps = ["CategoricalArrays", "Compat", "DataAPI", "Future", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrettyTables", "Printf", "REPL", "Reexport", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
-git-tree-sha1 = "b0db5579803eabb33f1274ca7ca2f472fdfb7f2a"
+deps = ["Compat", "DataAPI", "Future", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrettyTables", "Printf", "REPL", "Reexport", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
+git-tree-sha1 = "cfdfef912b7f93e4b848e80b9befdf9e331bc05a"
 uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-version = "0.22.5"
+version = "1.3.1"
 
 [[DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "4437b64df1e0adccc3e5d1adbc3ac741095e4677"
+git-tree-sha1 = "3daef5523dd2e769dad2365274f760ff5f282c7d"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.18.9"
+version = "0.18.11"
 
 [[DataValueInterfaces]]
 git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
@@ -123,16 +144,16 @@ uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 version = "1.0.3"
 
 [[DiffRules]]
-deps = ["NaNMath", "Random", "SpecialFunctions"]
-git-tree-sha1 = "214c3fcac57755cfda163d91c58893a8723f93e9"
+deps = ["LogExpFunctions", "NaNMath", "Random", "SpecialFunctions"]
+git-tree-sha1 = "9bc5dac3c8b6706b58ad5ce24cffd9861f07c94f"
 uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
-version = "1.0.2"
+version = "1.9.0"
 
 [[Distances]]
-deps = ["LinearAlgebra", "Statistics"]
-git-tree-sha1 = "366715149014943abd71aa647a07a43314158b2d"
+deps = ["LinearAlgebra", "SparseArrays", "Statistics", "StatsAPI"]
+git-tree-sha1 = "3258d0659f812acde79e8a74b11f17ac06d0ca04"
 uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.10.2"
+version = "0.10.7"
 
 [[Distributed]]
 deps = ["Random", "Serialization", "Sockets"]
@@ -140,21 +161,25 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 
 [[Distributions]]
 deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "f0e06a5b5ccda38e2fb8f59d91316e657b67047d"
+git-tree-sha1 = "a837fdf80f333415b69684ba8e8ae6ba76de6aaa"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.24.12"
+version = "0.24.18"
 
 [[DocStringExtensions]]
-deps = ["LibGit2", "Markdown", "Pkg", "Test"]
-git-tree-sha1 = "50ddf44c53698f5e784bbebb3f4b21c5807401b1"
+deps = ["LibGit2"]
+git-tree-sha1 = "b19534d1895d702889b219c382a6e18010797f0b"
 uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
-version = "0.8.3"
+version = "0.8.6"
+
+[[Downloads]]
+deps = ["ArgTools", "LibCURL", "NetworkOptions"]
+uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 
 [[ElasticArrays]]
 deps = ["Adapt"]
-git-tree-sha1 = "395e36b5962bd11bf080f4fe06546cd26a34e0d8"
+git-tree-sha1 = "a0fcc1bb3c9ceaf07e1d0529c9806ce94be6adf9"
 uuid = "fdbdab4c-e67f-52f5-8c3f-e7b388dad3d4"
-version = "1.2.6"
+version = "1.2.9"
 
 [[ElasticPDMats]]
 deps = ["LinearAlgebra", "MacroTools", "PDMats"]
@@ -163,30 +188,30 @@ uuid = "2904ab23-551e-5aed-883f-487f97af5226"
 version = "0.2.2"
 
 [[EnsembleKalmanProcesses]]
-deps = ["Distributions", "DocStringExtensions", "LinearAlgebra", "Random", "Statistics", "StatsBase"]
-git-tree-sha1 = "4b8328fecf09cb427e18b319edaf9989f8389fed"
+deps = ["Convex", "Distributions", "DocStringExtensions", "LinearAlgebra", "Random", "SCS", "SparseArrays", "Statistics", "StatsBase"]
+git-tree-sha1 = "8d64c688e053de72dbe24d3ecc2945ee51c8930c"
 repo-rev = "main"
 repo-url = "https://github.com/CliMA/EnsembleKalmanProcesses.jl"
 uuid = "aa8a2aa5-91d8-4396-bcef-d4f2ec43552d"
-version = "0.1.0"
+version = "0.1.2"
 
 [[FastGaussQuadrature]]
 deps = ["LinearAlgebra", "SpecialFunctions", "StaticArrays"]
-git-tree-sha1 = "5829b25887e53fb6730a9df2ff89ed24baa6abf6"
+git-tree-sha1 = "58d83dd5a78a36205bdfddb82b1bb67682e64487"
 uuid = "442a2c76-b920-505d-bb47-c5924d526838"
-version = "0.4.7"
+version = "0.4.9"
 
 [[FillArrays]]
 deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "ff537e5a3cba92fb48f30fec46723510450f2c0e"
+git-tree-sha1 = "693210145367e7685d8604aee33d9bfb85db8b31"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.10.2"
+version = "0.11.9"
 
 [[FiniteDiff]]
 deps = ["ArrayInterface", "LinearAlgebra", "Requires", "SparseArrays", "StaticArrays"]
-git-tree-sha1 = "f6f80c8f934efd49a286bb5315360be66956dfc4"
+git-tree-sha1 = "8b3c09b56acaf3c0e581c66638b85c8650ee9dca"
 uuid = "6a86dc24-6348-571c-b903-95158fe2bd41"
-version = "2.8.0"
+version = "2.8.1"
 
 [[Formatting]]
 deps = ["Printf"]
@@ -195,46 +220,50 @@ uuid = "59287772-0a20-5a39-b81b-1366585eb4c0"
 version = "0.4.2"
 
 [[ForwardDiff]]
-deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
-git-tree-sha1 = "d48a40c0f54f29a5c8748cfb3225719accc72b77"
+deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions", "StaticArrays"]
+git-tree-sha1 = "2b72a5624e289ee18256111657663721d59c143e"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.16"
+version = "0.10.24"
 
 [[Future]]
 deps = ["Random"]
 uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 
 [[GaussianProcesses]]
-deps = ["Distances", "Distributions", "ElasticArrays", "ElasticPDMats", "FastGaussQuadrature", "ForwardDiff", "LinearAlgebra", "Optim", "PDMats", "Printf", "ProgressMeter", "Random", "RecipesBase", "ScikitLearnBase", "SpecialFunctions", "StaticArrays", "Statistics", "StatsFuns", "Zygote"]
-git-tree-sha1 = "06f890e71136832f1c577aa443d3777ffa9594fa"
+deps = ["Distances", "Distributions", "ElasticArrays", "ElasticPDMats", "FastGaussQuadrature", "ForwardDiff", "LinearAlgebra", "Optim", "PDMats", "Printf", "ProgressMeter", "Random", "RecipesBase", "ScikitLearnBase", "SpecialFunctions", "StaticArrays", "Statistics", "StatsFuns"]
+git-tree-sha1 = "9cf8ba8037e332b1be14c71e549143e68c42a22d"
 uuid = "891a1506-143c-57d2-908e-e1f8e92e6de9"
-version = "0.12.3"
-
-[[IRTools]]
-deps = ["InteractiveUtils", "MacroTools", "Test"]
-git-tree-sha1 = "c67e7515a11f726f44083e74f218d134396d6510"
-uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
-version = "0.4.2"
+version = "0.12.4"
 
 [[IfElse]]
-git-tree-sha1 = "28e837ff3e7a6c3cdb252ce49fb412c8eb3caeef"
+git-tree-sha1 = "debdd00ffef04665ccbb3e150747a77560e8fad1"
 uuid = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
-version = "0.1.0"
+version = "0.1.1"
 
 [[InteractiveUtils]]
 deps = ["Markdown"]
 uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 
-[[InvertedIndices]]
+[[InverseFunctions]]
 deps = ["Test"]
-git-tree-sha1 = "15732c475062348b0165684ffe28e85ea8396afc"
+git-tree-sha1 = "a7254c0acd8e62f1ac75ad24d5db43f5f19f3c65"
+uuid = "3587e190-3f89-42d0-90ee-14403ec27112"
+version = "0.1.2"
+
+[[InvertedIndices]]
+git-tree-sha1 = "bee5f1ef5bf65df56bdd2e40447590b272a5471f"
 uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
-version = "1.0.0"
+version = "1.1.0"
+
+[[IrrationalConstants]]
+git-tree-sha1 = "7fd44fd4ff43fc60815f8e764c0f352b83c49151"
+uuid = "92d709cd-6900-40b7-9082-c6be49f344b6"
+version = "0.1.1"
 
 [[IterTools]]
-git-tree-sha1 = "05110a2ab1fc5f932622ffea2a003221f4782c18"
+git-tree-sha1 = "fa6287a4469f5e048d763df38279ee729fbd44e5"
 uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
-version = "1.3.0"
+version = "1.4.0"
 
 [[IteratorInterfaceExtensions]]
 git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856"
@@ -242,20 +271,39 @@ uuid = "82899510-4779-5014-852e-03e436cf321d"
 version = "1.0.0"
 
 [[JLLWrappers]]
-git-tree-sha1 = "a431f5f2ca3f4feef3bd7a5e94b8b8d4f2f647a0"
+deps = ["Preferences"]
+git-tree-sha1 = "642a199af8b68253517b80bd3bfd17eb4e84df6e"
 uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210"
-version = "1.2.0"
+version = "1.3.0"
 
 [[JSON]]
 deps = ["Dates", "Mmap", "Parsers", "Unicode"]
-git-tree-sha1 = "81690084b6198a2e1da36fcfda16eeca9f9f24e4"
+git-tree-sha1 = "8076680b162ada2a031f707ac7b4953e30667a37"
 uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-version = "0.21.1"
+version = "0.21.2"
+
+[[LDLFactorizations]]
+deps = ["AMD", "LinearAlgebra", "SparseArrays", "Test"]
+git-tree-sha1 = "399bbe845e06e1c2d44ebb241f554d45eaf66788"
+uuid = "40e66cde-538c-5869-a4ad-c39174c6795b"
+version = "0.8.1"
+
+[[LibCURL]]
+deps = ["LibCURL_jll", "MozillaCACerts_jll"]
+uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
+
+[[LibCURL_jll]]
+deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
+uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
 
 [[LibGit2]]
-deps = ["Printf"]
+deps = ["Base64", "NetworkOptions", "Printf", "SHA"]
 uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
 
+[[LibSSH2_jll]]
+deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
+uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
+
 [[Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 
@@ -269,73 +317,91 @@ version = "7.1.1"
 deps = ["Libdl"]
 uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
+[[LogExpFunctions]]
+deps = ["ChainRulesCore", "ChangesOfVariables", "DocStringExtensions", "InverseFunctions", "IrrationalConstants", "LinearAlgebra"]
+git-tree-sha1 = "e5718a00af0ab9756305a0392832c8952c7426c1"
+uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
+version = "0.3.6"
+
 [[Logging]]
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 
-[[LoopVectorization]]
-deps = ["DocStringExtensions", "LinearAlgebra", "OffsetArrays", "SIMDPirates", "SLEEFPirates", "UnPack", "VectorizationBase"]
-git-tree-sha1 = "3242a8f411e19eda9adc49d0b877681975c11375"
-uuid = "bdcacae8-1622-11e9-2a5c-532679323890"
-version = "0.8.26"
-
 [[MacroTools]]
 deps = ["Markdown", "Random"]
-git-tree-sha1 = "6a8a2a625ab0dea913aba95c11370589e0239ff0"
+git-tree-sha1 = "3d3e902b31198a27340d0bf00d6ac452866021cf"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.6"
+version = "0.5.9"
 
 [[Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
+[[MathOptInterface]]
+deps = ["BenchmarkTools", "CodecBzip2", "CodecZlib", "JSON", "LinearAlgebra", "MutableArithmetics", "OrderedCollections", "Printf", "SparseArrays", "Test", "Unicode"]
+git-tree-sha1 = "92b7de61ecb616562fd2501334f729cc9db2a9a6"
+uuid = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
+version = "0.10.6"
+
+[[MbedTLS_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
+
 [[Missings]]
 deps = ["DataAPI"]
-git-tree-sha1 = "f8c673ccc215eb50fcadb285f522420e29e69e1c"
+git-tree-sha1 = "bf210ce90b6c9eed32d25dbcae1ebc565df2687f"
 uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.4.5"
+version = "1.0.2"
 
 [[Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
+[[MozillaCACerts_jll]]
+uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
+
+[[MutableArithmetics]]
+deps = ["LinearAlgebra", "SparseArrays", "Test"]
+git-tree-sha1 = "7bb6853d9afec54019c1397c6eb610b9b9a19525"
+uuid = "d8a4904e-b15c-11e9-3269-09a3773c0cb0"
+version = "0.3.1"
+
 [[NLSolversBase]]
 deps = ["DiffResults", "Distributed", "FiniteDiff", "ForwardDiff"]
-git-tree-sha1 = "39d6bc45e99c96e6995cbddac02877f9b61a1dd1"
+git-tree-sha1 = "50310f934e55e5ca3912fb941dec199b49ca9b68"
 uuid = "d41bc354-129a-5804-8e4c-c37616107c6c"
-version = "7.7.1"
-
-[[NNlib]]
-deps = ["ChainRulesCore", "Compat", "LinearAlgebra", "Pkg", "Requires", "Statistics"]
-git-tree-sha1 = "5ce2e4b2bfe3811811e7db4b6a148439806fd2f8"
-uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.7.16"
+version = "7.8.2"
 
 [[NaNMath]]
-git-tree-sha1 = "bfe47e760d60b82b66b61d2d44128b62e3a369fb"
+git-tree-sha1 = "f755f36b19a5116bb580de457cda0c140153f283"
 uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
-version = "0.3.5"
+version = "0.3.6"
 
-[[OffsetArrays]]
-deps = ["Adapt"]
-git-tree-sha1 = "b3dfef5f2be7d7eb0e782ba9146a5271ee426e90"
-uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
-version = "1.6.2"
+[[NetworkOptions]]
+uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
+
+[[OpenBLAS_jll]]
+deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
+uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
+
+[[OpenLibm_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
 
 [[OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "9db77584158d0ab52307f8c04f8e7c08ca76b5b3"
+git-tree-sha1 = "13652491f6856acfd2db29360e1bbcd4565d04f1"
 uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
-version = "0.5.3+4"
+version = "0.5.5+0"
 
 [[Optim]]
-deps = ["Compat", "FillArrays", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"]
-git-tree-sha1 = "3286df38aba45acf7445f3acd87b7b57b7c7feb7"
+deps = ["Compat", "FillArrays", "ForwardDiff", "LineSearches", "LinearAlgebra", "NLSolversBase", "NaNMath", "Parameters", "PositiveFactorizations", "Printf", "SparseArrays", "StatsBase"]
+git-tree-sha1 = "35d435b512fbab1d1a29138b5229279925eba369"
 uuid = "429524aa-4258-5aef-a3af-852621145aeb"
-version = "1.2.4"
+version = "1.5.0"
 
 [[OrderedCollections]]
-git-tree-sha1 = "4fa2ba51070ec13fcc7517db714445b4ab986bdf"
+git-tree-sha1 = "85f8e6578bf1f9ee0d11e7bb1b1456435479d47c"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.4.0"
+version = "1.4.1"
 
 [[PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse", "Test"]
@@ -345,25 +411,25 @@ version = "0.10.1"
 
 [[Parameters]]
 deps = ["OrderedCollections", "UnPack"]
-git-tree-sha1 = "2276ac65f1e236e0a6ea70baff3f62ad4c625345"
+git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe"
 uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a"
-version = "0.12.2"
+version = "0.12.3"
 
 [[Parsers]]
 deps = ["Dates"]
-git-tree-sha1 = "223a825cccef2228f3fdbf2ecc7ca93363059073"
+git-tree-sha1 = "d7fa6237da8004be601e19bd6666083056649918"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "1.0.16"
+version = "2.1.3"
 
 [[Pkg]]
-deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
+deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 
 [[PooledArrays]]
 deps = ["DataAPI", "Future"]
-git-tree-sha1 = "cde4ce9d6f33219465b55162811d8de8139c0414"
+git-tree-sha1 = "db3a23166af8aebf4db5ef87ac5b00d36eb771e2"
 uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
-version = "1.2.1"
+version = "1.4.0"
 
 [[PositiveFactorizations]]
 deps = ["LinearAlgebra"]
@@ -371,36 +437,46 @@ git-tree-sha1 = "17275485f373e6673f7e7f97051f703ed5b15b20"
 uuid = "85a6dd25-e78a-55b7-8502-1745935b8125"
 version = "0.2.4"
 
+[[Preferences]]
+deps = ["TOML"]
+git-tree-sha1 = "00cfd92944ca9c760982747e9a1d0d5d86ab1e5a"
+uuid = "21216c6a-2e73-6563-6e65-726566657250"
+version = "1.2.2"
+
 [[PrettyTables]]
 deps = ["Crayons", "Formatting", "Markdown", "Reexport", "Tables"]
-git-tree-sha1 = "574a6b3ea95f04e8757c0280bb9c29f1a5e35138"
+git-tree-sha1 = "dfb54c4e414caa595a1f2ed759b160f5a3ddcba5"
 uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-version = "0.11.1"
+version = "1.3.1"
 
 [[Printf]]
 deps = ["Unicode"]
 uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 
+[[Profile]]
+deps = ["Printf"]
+uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
+
 [[ProgressMeter]]
 deps = ["Distributed", "Printf"]
-git-tree-sha1 = "6e9c89cba09f6ef134b00e10625590746ba1e036"
+git-tree-sha1 = "afadeba63d90ff223a6a48d2009434ecee2ec9e8"
 uuid = "92933f4c-e287-5a05-a399-4b506db050ca"
-version = "1.5.0"
+version = "1.7.1"
 
 [[PyCall]]
 deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Serialization", "VersionParsing"]
-git-tree-sha1 = "dd1a970b543bd02efce2984582e996af28cab27f"
+git-tree-sha1 = "71fd4022ecd0c6d20180e23ff1b3e05a143959c2"
 uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
-version = "1.92.2"
+version = "1.93.0"
 
 [[QuadGK]]
 deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "12fbe86da16df6679be7521dfb39fbc861e1dc7b"
+git-tree-sha1 = "78aadffb3efd2155af139781b8a8df1ef279ea39"
 uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.4.1"
+version = "2.4.2"
 
 [[REPL]]
-deps = ["InteractiveUtils", "Markdown", "Sockets"]
+deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
 uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 
 [[Random]]
@@ -408,53 +484,59 @@ deps = ["Serialization"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [[RecipesBase]]
-git-tree-sha1 = "b3fb709f3c97bfc6e948be68beeecb55a0b340ae"
+git-tree-sha1 = "6bf3f380ff52ce0832ddd3a2a7b9538ed1bcca7d"
 uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
-version = "1.1.1"
+version = "1.2.1"
 
 [[Reexport]]
-git-tree-sha1 = "57d8440b0c7d98fc4f889e478e80f268d534c9d5"
+git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
 uuid = "189a3867-3050-52da-a836-e630ba90ab69"
-version = "1.0.0"
+version = "1.2.2"
 
 [[Requires]]
 deps = ["UUIDs"]
-git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621"
+git-tree-sha1 = "8f82019e525f4d5c669692772a6f4b0a58b06a6a"
 uuid = "ae029012-a4dd-5104-9daa-d747884805df"
-version = "1.1.3"
+version = "1.2.0"
 
 [[Rmath]]
 deps = ["Random", "Rmath_jll"]
-git-tree-sha1 = "86c5647b565873641538d8f812c04e4c9dbeb370"
+git-tree-sha1 = "bf3188feca147ce108c76ad82c2792c57abe7b1f"
 uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa"
-version = "0.6.1"
+version = "0.7.0"
 
 [[Rmath_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "d76185aa1f421306dec73c057aa384bad74188f0"
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "68db32dff12bb6127bac73c209881191bf0efbb7"
 uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
-version = "0.2.2+1"
+version = "0.3.0+0"
+
+[[SCS]]
+deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "MathOptInterface", "Requires", "SCS_GPU_jll", "SCS_jll", "SparseArrays"]
+git-tree-sha1 = "c819d023621358f3c08f08d41bd9354cf1357d35"
+uuid = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
+version = "0.8.1"
+
+[[SCS_GPU_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenBLAS_jll", "Pkg"]
+git-tree-sha1 = "a96402e3b494a8bbec61b1adb86d4be04112c646"
+uuid = "af6e375f-46ec-5fa0-b791-491b0dfa44a4"
+version = "2.1.4+0"
+
+[[SCS_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenBLAS_jll", "Pkg"]
+git-tree-sha1 = "66c07568ecec96260aaac194ee1e5ee8df62f386"
+uuid = "f4f2fc5b-1d94-523c-97ea-2ab488bedf4b"
+version = "2.1.4+0"
 
 [[SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 
-[[SIMDPirates]]
-deps = ["VectorizationBase"]
-git-tree-sha1 = "a1b418634d6478bf2163920eae3b536dcc768626"
-uuid = "21efa798-c60a-11e8-04d3-e1a92915a26a"
-version = "0.8.26"
-
-[[SLEEFPirates]]
-deps = ["Libdl", "SIMDPirates", "VectorizationBase"]
-git-tree-sha1 = "67ae90a18aa8c22bf159318300e765fbd89ddf6e"
-uuid = "476501e8-09a2-5ece-8869-fb82de89a1fa"
-version = "0.5.5"
-
 [[ScikitLearn]]
 deps = ["Compat", "Conda", "DataFrames", "Distributed", "IterTools", "LinearAlgebra", "MacroTools", "Parameters", "Printf", "PyCall", "Random", "ScikitLearnBase", "SparseArrays", "StatsBase", "VersionParsing"]
-git-tree-sha1 = "bf4c558a0d0f1129b23e80f1ca772a9db457df4d"
+git-tree-sha1 = "ccb822ff4222fcf6ff43bbdbd7b80332690f168e"
 uuid = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
-version = "0.6.3"
+version = "0.6.4"
 
 [[ScikitLearnBase]]
 deps = ["LinearAlgebra", "Random", "Statistics"]
@@ -473,75 +555,88 @@ uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
 [[SortingAlgorithms]]
-deps = ["DataStructures", "Random", "Test"]
-git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd"
+deps = ["DataStructures"]
+git-tree-sha1 = "b3363d7460f7d098ca0912c69b082f75625d7508"
 uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
-version = "0.3.1"
+version = "1.0.1"
 
 [[SparseArrays]]
 deps = ["LinearAlgebra", "Random"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [[SpecialFunctions]]
-deps = ["ChainRulesCore", "OpenSpecFun_jll"]
-git-tree-sha1 = "5919936c0e92cff40e57d0ddf0ceb667d42e5902"
+deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
+git-tree-sha1 = "f0bccf98e16759818ffc5d97ac3ebf87eb950150"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "1.3.0"
+version = "1.8.1"
 
 [[Static]]
 deps = ["IfElse"]
-git-tree-sha1 = "e59fea643e12d4fa39098c833bd07cd0cd950297"
+git-tree-sha1 = "7f5a513baec6f122401abfc8e9c074fdac54f6c1"
 uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
-version = "0.2.3"
+version = "0.4.1"
 
 [[StaticArrays]]
 deps = ["LinearAlgebra", "Random", "Statistics"]
-git-tree-sha1 = "9da72ed50e94dbff92036da395275ed114e04d49"
+git-tree-sha1 = "3c76dde64d03699e074ac02eb2e8ba8254d428da"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "1.0.1"
+version = "1.2.13"
 
 [[Statistics]]
 deps = ["LinearAlgebra", "SparseArrays"]
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
+[[StatsAPI]]
+git-tree-sha1 = "0f2aa8e32d511f758a2ce49208181f7733a0936a"
+uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
+version = "1.1.0"
+
 [[StatsBase]]
-deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
-git-tree-sha1 = "7bab7d4eb46b225b35179632852b595a3162cb61"
+deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
+git-tree-sha1 = "2bb0cb32026a66037360606510fca5984ccc6b75"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.33.2"
+version = "0.33.13"
 
 [[StatsFuns]]
-deps = ["Rmath", "SpecialFunctions"]
-git-tree-sha1 = "3b9f665c70712af3264b61c27a7e1d62055dafd1"
+deps = ["ChainRulesCore", "InverseFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
+git-tree-sha1 = "bedb3e17cc1d94ce0e6e66d3afa47157978ba404"
 uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-version = "0.9.6"
-
-[[StructTypes]]
-deps = ["Dates", "UUIDs"]
-git-tree-sha1 = "d7f4287dbc1e590265f50ceda1b40ed2bb31bbbb"
-uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
-version = "1.4.0"
+version = "0.9.14"
 
 [[SuiteSparse]]
 deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
 uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 
+[[TOML]]
+deps = ["Dates"]
+uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
+
 [[TableTraits]]
 deps = ["IteratorInterfaceExtensions"]
-git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e"
+git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
 uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
-version = "1.0.0"
+version = "1.0.1"
 
 [[Tables]]
 deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
-git-tree-sha1 = "a716dde43d57fa537a19058d044b495301ba6565"
+git-tree-sha1 = "bb1064c9a84c52e277f1096cf41434b675cd368b"
 uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-version = "1.3.2"
+version = "1.6.1"
+
+[[Tar]]
+deps = ["ArgTools", "SHA"]
+uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
 
 [[Test]]
-deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
+deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
+[[TranscodingStreams]]
+deps = ["Random", "Test"]
+git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c"
+uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
+version = "0.9.6"
+
 [[UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
@@ -554,25 +649,19 @@ version = "1.0.2"
 [[Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 
-[[VectorizationBase]]
-deps = ["CpuId", "Libdl", "LinearAlgebra"]
-git-tree-sha1 = "03e2fbb479a1ea350398195b6fbf439bae0f8260"
-uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
-version = "0.12.33"
-
 [[VersionParsing]]
-git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f"
+git-tree-sha1 = "e575cf85535c7c3292b4d89d89cc29e8c3098e47"
 uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
-version = "1.2.0"
+version = "1.2.1"
+
+[[Zlib_jll]]
+deps = ["Libdl"]
+uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
+
+[[nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
 
-[[Zygote]]
-deps = ["AbstractFFTs", "ArrayLayouts", "ChainRules", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "LoopVectorization", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "18f758f28ca2c236e449be64e366e201965129a7"
-uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.5.17"
-
-[[ZygoteRules]]
-deps = ["MacroTools"]
-git-tree-sha1 = "9e7a1e8ca60b742e508a315c17eef5211e7fbfd7"
-uuid = "700de1a5-db45-46bc-99cf-38207098b444"
-version = "0.2.1"
+[[p7zip_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
diff --git a/docs/make.jl b/docs/make.jl
index e5806af48..c90af5b4c 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -7,9 +7,12 @@ using Documenter
 
 #----------
 api = ["CalibrateEmulateSample" => [
-         "GPEmulator" => "API/GaussianProcessEmulator.md",
-				 "MarkovChainMonteCarlo" => "API/MarkovChainMonteCarlo.md", 
-				 "Utilities" => "API/Utilities.md"
+    "Emulators" => [
+        "General Emulator" => "API/Emulators.md",
+        "Gaussian Process" => "API/GaussianProcess.md",
+        ],
+    "MarkovChainMonteCarlo" => "API/MarkovChainMonteCarlo.md", 
+    "Utilities" => "API/Utilities.md"
          ],
 ]
 
diff --git a/docs/src/API/Emulators.md b/docs/src/API/Emulators.md
new file mode 100644
index 000000000..f4c97aa11
--- /dev/null
+++ b/docs/src/API/Emulators.md
@@ -0,0 +1,17 @@
+# Emulators
+
+```@meta
+CurrentModule = CalibrateEmulateSample.Emulators
+```
+
+```@docs
+Decomposition
+Emulator
+optimize_hyperparameters!(::Emulator)
+predict
+normalize
+standardize
+reverse_standardize
+svd_transform
+svd_reverse_transform_mean_cov
+```
\ No newline at end of file
diff --git a/docs/src/API/GaussianProcess.md b/docs/src/API/GaussianProcess.md
new file mode 100644
index 000000000..79be9e7c0
--- /dev/null
+++ b/docs/src/API/GaussianProcess.md
@@ -0,0 +1,13 @@
+# GaussianProcess
+
+```@meta
+CurrentModule = CalibrateEmulateSample.Emulators
+```
+
+```@docs
+GaussianProcessesPackage
+PredictionType
+GaussianProcess
+build_models!
+optimize_hyperparameters!(::GaussianProcess{GPJL})
+```
\ No newline at end of file
diff --git a/docs/src/API/GaussianProcessEmulator.md b/docs/src/API/GaussianProcessEmulator.md
deleted file mode 100644
index a97b37f83..000000000
--- a/docs/src/API/GaussianProcessEmulator.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# GaussianProcessEmulator
-
-```@meta
-CurrentModule = CalibrateEmulateSample.GaussianProcessEmulator
-```
-
-```@docs
-GaussianProcessesPackage
-PredictionType
-GaussianProcess
-predict
-svd_transform
-svd_reverse_transform_mean_cov
-```
\ No newline at end of file
diff --git a/examples/Emulator/GaussianProcess/Manifest.toml b/examples/Emulator/GaussianProcess/Manifest.toml
new file mode 100644
index 000000000..941b6abe4
--- /dev/null
+++ b/examples/Emulator/GaussianProcess/Manifest.toml
@@ -0,0 +1,1149 @@
+# This file is machine-generated - editing it directly is not advised
+
+[[Adapt]]
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+
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+
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+
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+
+[[ChainRulesCore]]
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+git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
+uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
+version = "1.3.7+0"
+
+[[nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
+
+[[p7zip_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+
+[[x264_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
+uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
+version = "2021.5.5+0"
+
+[[x265_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
+uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
+version = "3.5.0+0"
+
+[[xkbcommon_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
+git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
+uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
+version = "0.9.1+5"
diff --git a/examples/GaussianProcessEmulator/Project.toml b/examples/Emulator/GaussianProcess/Project.toml
similarity index 76%
rename from examples/GaussianProcessEmulator/Project.toml
rename to examples/Emulator/GaussianProcess/Project.toml
index 00a75c4d8..4bb6a44ed 100644
--- a/examples/GaussianProcessEmulator/Project.toml
+++ b/examples/Emulator/GaussianProcess/Project.toml
@@ -1,8 +1,10 @@
 [deps]
+CalibrateEmulateSample = "95e48a1f-0bec-4818-9538-3db4340308e3"
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
diff --git a/examples/GaussianProcessEmulator/plot_GP.jl b/examples/Emulator/GaussianProcess/plot_GP.jl
similarity index 65%
rename from examples/GaussianProcessEmulator/plot_GP.jl
rename to examples/Emulator/GaussianProcess/plot_GP.jl
index 9955e3346..b9eafb466 100644
--- a/examples/GaussianProcessEmulator/plot_GP.jl
+++ b/examples/Emulator/GaussianProcess/plot_GP.jl
@@ -7,11 +7,19 @@ using Random
 using Distributions
 using Statistics
 using LinearAlgebra
-using CalibrateEmulateSample.GaussianProcessEmulator
+using CalibrateEmulateSample.Emulators
 using CalibrateEmulateSample.DataStorage
 
-using Plots; pyplot(size=(1500, 700))
-Plots.scalefontsizes(1.3)
+plot_flag = true
+if plot_flag
+    using Plots
+    gr(size=(1500, 700))
+    Plots.scalefontsizes(1.3)
+    font = Plots.font("Helvetica", 18)
+    fontdict = Dict(:guidefont=>font, :xtickfont=>font, :ytickfont=>font, 
+                    :legendfont=>font)
+
+end
 
 ###############################################################################
 #                                                                             #
@@ -61,8 +69,10 @@ if !isdir(output_directory)
     mkdir(output_directory)
 end
 
+#create the machine learning tools: Gaussian Process
 gppackage = GPJL()
 pred_type = YType()
+gaussian_process = GaussianProcess(gppackage,noise_learn=true)
 
 # Generate training data (x-y pairs, where x ∈ ℝ ᵖ, y ∈ ℝ ᵈ)
 # x = [x1, x2]: inputs/predictors/features/parameters
@@ -70,7 +80,7 @@ pred_type = YType()
 # The observables y are related to the parameters x by: 
 # y = G(x1, x2) + η, 
 # where G(x1, x2) := [sin(x1) + cos(x2), sin(x1) - cos(x2)], and η ~ N(0, Σ)
-n = 50  # number of training points
+n = 100  # number of training points
 p = 2   # input dim 
 d = 2   # output dim
 
@@ -81,7 +91,7 @@ g2x = sin.(X[1, :]) .- cos.(X[2, :])
 gx = zeros(2,n)
 gx[1,:] = g1x
 gx[2,:] = g2x
-
+    
 # Add noise η
 μ = zeros(d) 
 Σ = 0.1 * [[0.8, 0.0] [0.0, 0.5]] # d x d
@@ -89,70 +99,80 @@ noise_samples = rand(MvNormal(μ, Σ), n)
 # y = G(x) + η
 Y = gx .+ noise_samples
 
+
 #plot training data with and without noise
-p1 = plot(X[1,:], X[2,:], g1x, st=:surface, camera=(-30, 30), c=:cividis, 
+if plot_flag
+    p1 = plot(X[1,:], X[2,:], g1x, st=:surface, camera=(30, 60), c=:cividis, 
               xlabel="x1", ylabel="x2",
               zguidefontrotation=90)
-figpath = joinpath(output_directory, "GP_test_observed_y1nonoise.png")
-savefig(figpath)
-linkfig(figpath)
-
-p2 = plot(X[1,:], X[2,:], g2x, st=:surface, camera=(-30, 30), c=:cividis, 
+    
+    figpath = joinpath(output_directory, "GP_test_observed_y1nonoise.png")
+    savefig(figpath)
+    
+    p2 = plot(X[1,:], X[2,:], g2x, st=:surface, camera=(30, 60), c=:cividis, 
               xlabel="x1", ylabel="x2",
               zguidefontrotation=90)
-figpath = joinpath(output_directory, "GP_test_observed_y2nonoise.png")
-savefig(figpath)
-linkfig(figpath)
-
-p1 = plot(X[1,:], X[2,:], Y[1,:], st=:surface, camera=(-30, 30), c=:cividis, 
+    figpath = joinpath(output_directory, "GP_test_observed_y2nonoise.png")
+    savefig(figpath)
+    
+    p1 = plot(X[1,:], X[2,:], Y[1,:], st=:surface, camera=(30, 60), c=:cividis, 
               xlabel="x1", ylabel="x2",
               zguidefontrotation=90)
-figpath = joinpath(output_directory, "GP_test_observed_y1.png")
-savefig(figpath)
-linkfig(figpath)
-
-p2 = plot(X[1,:], X[2,:], Y[2,:], st=:surface, camera=(-30, 30), c=:cividis, 
+    figpath = joinpath(output_directory, "GP_test_observed_y1.png")
+    savefig(figpath)
+    
+    p2 = plot(X[1,:], X[2,:], Y[2,:], st=:surface, camera=(30, 60), c=:cividis, 
               xlabel="x1", ylabel="x2",
               zguidefontrotation=90)
-figpath = joinpath(output_directory, "GP_test_observed_y2.png")
-savefig(figpath)
-linkfig(figpath)
+    figpath = joinpath(output_directory, "GP_test_observed_y2.png")
+    savefig(figpath)
+
+end
 
 iopairs = PairedDataContainer(X,Y,data_are_columns=true)
 @assert get_inputs(iopairs) == X
 @assert get_outputs(iopairs) == Y
 
-# Fit 2D Gaussian Process regression model
+# Create the emulator class, and build the  2D Gaussian Process regression model
 # (To be precise: We fit two models, one that predicts y1 from x1 and x2, 
 # and one that predicts y2 from x1 and x2)
 # Setting GPkernel=nothing leads to the creation of a default squared 
 # exponential kernel. 
 # Setting noise_learn=true leads to the addition of white noise to the
 # kernel
-gpobj = GaussianProcess(iopairs, gppackage, GPkernel=nothing, obs_noise_cov=Σ, 
-              normalized=true, noise_learn=true, prediction_type=pred_type)
+emulator = Emulator(
+    gaussian_process,
+    iopairs,
+    obs_noise_cov=Σ,
+    normalize_inputs=false)
+println("build GP with ", n, " training points")
+
+#optimize the hyperparameters to best fit the GP.
+optimize_hyperparameters!(emulator)
+println("GP trained")
+
+#gpobj = GaussianProcess(iopairs, gppackage, GPkernel=nothing, obs_noise_cov=Σ, 
+#              normalized=true, noise_learn=true, prediction_type=pred_type)
 
 # Plot mean and variance of the predicted observables y1 and y2
 # For this, we generate test points on a x1-x2 grid.
-n_pts = 100
+n_pts = 50
 x1 = range(0.0, stop=2*π, length=n_pts)
 x2 = range(0.0, stop=2*π, length=n_pts) 
 X1, X2 = meshgrid(x1, x2)
 # Input for predict has to be of size N_samples x input_dim
 inputs = permutedims(hcat(X1[:], X2[:]),(2,1))
 
-font = Plots.font("Helvetica", 18)
-fontdict = Dict(:guidefont=>font, :xtickfont=>font, :ytickfont=>font, 
-                :legendfont=>font)
+#gp_mean, gp_cov = GaussianProcessEmulator.predict(gpobj, 
+#                                                  inputs, 
+#                                                  transform_to_real=true)
 # Predict on the grid points (note that `predict` returns the full
 # covariance matrices, not just the variance -- gp_cov is a vector
 # of covariance matrices)
-gp_mean, gp_cov = GaussianProcessEmulator.predict(gpobj, 
-                                                  inputs, 
-                                                  transform_to_real=true)
+
+gp_mean, gp_cov = predict(emulator, inputs, transform_to_real=true)
 println("end predictions at ", n_pts*n_pts, " points")
 
-println("start plotting...")
 
 #plot predictions
 for y_i in 1:d
@@ -161,42 +181,44 @@ for y_i in 1:d
     gp_var = permutedims(vcat([x' for x in gp_var_temp]...),(2,1)) # 2 x 40000
    
     mean_grid = reshape(gp_mean[y_i, :], n_pts, n_pts) # 2 x 40000
-    p1 = plot(x1, x2, mean_grid, st=:surface, camera=(-30, 30), c=:cividis, 
-              xlabel="x1", ylabel="x2", zlabel="mean of y"*string(y_i),
-              zguidefontrotation=90)
-
+    if plot_flag
+        p1 = plot(x1, x2, mean_grid, st=:surface, camera=(30, 60), c=:cividis, 
+                  xlabel="x1", ylabel="x2", zlabel="mean of y"*string(y_i),
+                  zguidefontrotation=90)
+    end
     var_grid = reshape(gp_var[y_i, :], n_pts, n_pts)
-    p2 = plot(x1, x2, var_grid, st=:surface, camera=(-30, 30), c=:cividis,
-              xlabel="x1", ylabel="x2", zlabel="var of y"*string(y_i),
-              zguidefontrotation=90)
+    if plot_flag
+        p2 = plot(x1, x2, var_grid, st=:surface, camera=(30, 60), c=:cividis,
+                  xlabel="x1", ylabel="x2", zlabel="var of y"*string(y_i),
+                  zguidefontrotation=90)
 
-    plot(p1, p2, layout=(1, 2), legend=false)
+        plot(p1, p2, layout=(1, 2), legend=false)
 
-    figpath = joinpath(output_directory, "GP_test_y"*string(y_i)*"_predictions.png")
-    savefig(figpath)
-    linkfig(figpath)
+        savefig(joinpath(output_directory, "GP_test_y"*string(y_i)*"_predictions.png"))
+    end
 end
 
 # Plot the true components of G(x1, x2)
 g1_true = sin.(inputs[1,:]) .+ cos.(inputs[2, :])
 g1_true_grid = reshape(g1_true, n_pts, n_pts)
-p3 = plot(x1, x2, g1_true_grid, st=:surface, camera=(-30, 30), c=:cividis, 
-          xlabel="x1", ylabel="x2", zlabel="sin(x1) + cos(x2)",
-          zguidefontrotation=90)
-figpath = joinpath(output_directory, "GP_test_true_g1.png")
-savefig(figpath)
-linkfig(figpath)
+if plot_flag
+    p3 = plot(x1, x2, g1_true_grid, st=:surface, camera=(30, 60), c=:cividis, 
+              xlabel="x1", ylabel="x2", zlabel="sin(x1) + cos(x2)",
+              zguidefontrotation=90)
+    savefig(joinpath(output_directory, "GP_test_true_g1.png"))
+end
 
 g2_true = sin.(inputs[1, :]) .- cos.(inputs[2, :])
 g2_true_grid = reshape(g2_true, n_pts, n_pts)
-p4 = plot(x1, x2, g2_true_grid, st=:surface, camera=(-30, 30), c=:cividis, 
-          xlabel="x1", ylabel="x2", zlabel="sin(x1) - cos(x2)",
-          zguidefontrotation=90)
-g_true_grids = [g1_true_grid, g2_true_grid]
-figpath = joinpath(output_directory, "GP_test_true_g2.png")
-savefig(figpath)
-linkfig(figpath)
+if plot_flag
+    p4 = plot(x1, x2, g2_true_grid, st=:surface, camera=(30, 60), c=:cividis, 
+              xlabel="x1", ylabel="x2", zlabel="sin(x1) - cos(x2)",
+              zguidefontrotation=90)
+    g_true_grids = [g1_true_grid, g2_true_grid]
+  
+    savefig(joinpath(output_directory, "GP_test_true_g2.png"))
 
+end
 
 # Plot the difference between the truth and the mean of the predictions
 for y_i in 1:d
@@ -208,15 +230,17 @@ for y_i in 1:d
     mean_grid = reshape(gp_mean[y_i,:], n_pts, n_pts)
     var_grid = reshape(gp_var[y_i, :], n_pts, n_pts)
     # Compute and plot 1/variance * (truth - prediction)^2
-    zlabel = "1/var * (true_y"*string(y_i)*" - predicted_y"*string(y_i)*")^2"
-    p5 = plot(x1, x2, 1.0 ./ var_grid .* (g_true_grids[y_i] .- mean_grid).^2, 
-              st=:surface, camera=(-30, 30), c=:magma, zlabel=zlabel,
-              xlabel="x1", ylabel="x2", 
-              zguidefontrotation=90)
 
-    figpath = joinpath(output_directory, "GP_test_y"*string(y_i)*"_difference_truth_prediction.png")
-    savefig(figpath)
-    linkfig(figpath)
+    if plot_flag
+        zlabel = "1/var * (true_y"*string(y_i)*" - predicted_y"*string(y_i)*")^2"
+        
+        p5 = plot(x1, x2, sqrt.(1.0 ./ var_grid .* (g_true_grids[y_i] .- mean_grid).^2), 
+                  st=:surface, camera=(30, 60), c=:magma, zlabel=zlabel,
+                  xlabel="x1", ylabel="x2", 
+                  zguidefontrotation=90)
+        
+        savefig(joinpath(output_directory, "GP_test_y"*string(y_i)*"_difference_truth_prediction.png"))
+    end
 end
 
-Plots.scalefontsizes(1/1.3)
+#Plots.scalefontsizes(1/1.3)
diff --git a/examples/GaussianProcessEmulator/Manifest.toml b/examples/GaussianProcessEmulator/Manifest.toml
deleted file mode 100644
index 126c9113f..000000000
--- a/examples/GaussianProcessEmulator/Manifest.toml
+++ /dev/null
@@ -1,836 +0,0 @@
-# This file is machine-generated - editing it directly is not advised
-
-[[Adapt]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "ffcfa2d345aaee0ef3d8346a073d5dd03c983ebe"
-uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "3.2.0"
-
-[[Artifacts]]
-deps = ["Pkg"]
-git-tree-sha1 = "c30985d8821e0cd73870b17b0ed0ce6dc44cb744"
-uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
-version = "1.3.0"
-
-[[Base64]]
-uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
-
-[[Bzip2_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "c3598e525718abcc440f69cc6d5f60dda0a1b61e"
-uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
-version = "1.0.6+5"
-
-[[Cairo_jll]]
-deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "e2f47f6d8337369411569fd45ae5753ca10394c6"
-uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a"
-version = "1.16.0+6"
-
-[[ChainRulesCore]]
-deps = ["Compat", "LinearAlgebra", "SparseArrays"]
-git-tree-sha1 = "de4f08843c332d355852721adb1592bce7924da3"
-uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "0.9.29"
-
-[[ColorSchemes]]
-deps = ["ColorTypes", "Colors", "FixedPointNumbers", "Random", "StaticArrays"]
-git-tree-sha1 = "3141757b5832ee7a0386db87997ee5a23ff20f4d"
-uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
-version = "3.10.2"
-
-[[ColorTypes]]
-deps = ["FixedPointNumbers", "Random"]
-git-tree-sha1 = "5e9769a17f17b587c951d57ba4319782b40c3513"
-uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
-version = "0.10.10"
-
-[[Colors]]
-deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
-git-tree-sha1 = "ac5f2213e56ed8a34a3dd2f681f4df1166b34929"
-uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
-version = "0.12.6"
-
-[[Compat]]
-deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
-git-tree-sha1 = "919c7f3151e79ff196add81d7f4e45d91bbf420b"
-uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "3.25.0"
-
-[[CompilerSupportLibraries_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "8e695f735fca77e9708e795eda62afdb869cbb70"
-uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "0.3.4+0"
-
-[[Conda]]
-deps = ["JSON", "VersionParsing"]
-git-tree-sha1 = "6231e40619c15148bcb80aa19d731e629877d762"
-uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
-version = "1.5.1"
-
-[[Contour]]
-deps = ["StaticArrays"]
-git-tree-sha1 = "9f02045d934dc030edad45944ea80dbd1f0ebea7"
-uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
-version = "0.5.7"
-
-[[DataAPI]]
-git-tree-sha1 = "dfb3b7e89e395be1e25c2ad6d7690dc29cc53b1d"
-uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.6.0"
-
-[[DataStructures]]
-deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "4437b64df1e0adccc3e5d1adbc3ac741095e4677"
-uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.18.9"
-
-[[DataValueInterfaces]]
-git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
-uuid = "e2d170a0-9d28-54be-80f0-106bbe20a464"
-version = "1.0.0"
-
-[[Dates]]
-deps = ["Printf"]
-uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
-
-[[DelimitedFiles]]
-deps = ["Mmap"]
-uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-
-[[Distributed]]
-deps = ["Random", "Serialization", "Sockets"]
-uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
-
-[[Distributions]]
-deps = ["FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SparseArrays", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "e64debe8cd174cc52d7dd617ebc5492c6f8b698c"
-uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.24.15"
-
-[[EarCut_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "92d8f9f208637e8d2d28c664051a00569c01493d"
-uuid = "5ae413db-bbd1-5e63-b57d-d24a61df00f5"
-version = "2.1.5+1"
-
-[[Expat_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "1402e52fcda25064f51c77a9655ce8680b76acf0"
-uuid = "2e619515-83b5-522b-bb60-26c02a35a201"
-version = "2.2.7+6"
-
-[[FFMPEG]]
-deps = ["FFMPEG_jll", "x264_jll"]
-git-tree-sha1 = "9a73ffdc375be61b0e4516d83d880b265366fe1f"
-uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
-version = "0.4.0"
-
-[[FFMPEG_jll]]
-deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "LibVPX_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "Pkg", "Zlib_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
-git-tree-sha1 = "3cc57ad0a213808473eafef4845a74766242e05f"
-uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5"
-version = "4.3.1+4"
-
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diff --git a/examples/GaussianProcessEmulator/learn_noise.jl b/examples/GaussianProcessEmulator/learn_noise.jl
deleted file mode 100644
index a5fbaf2e4..000000000
--- a/examples/GaussianProcessEmulator/learn_noise.jl
+++ /dev/null
@@ -1,141 +0,0 @@
-# Reference the in-tree version of CalibrateEmulateSample on Julias load path
-prepend!(LOAD_PATH, [joinpath(@__DIR__, "..", "..")])
-
-# Import modules
-using Random
-using Distributions
-using Statistics
-using LinearAlgebra
-using CalibrateEmulateSample.GaussianProcessEmulator
-using CalibrateEmulateSample.DataStorage
-
-###############################################################################
-#                                                                             #
-#   This examples shows how to fit a Gaussian Process (GP) regression model   #
-#   using GaussianProcessEmulator and demonstrates how the GaussianProcess's built-in Singular       #
-#   Value Decomposition (SVD) decorrelates the training data and maps into    #
-#   a space where the covariance of the observational noise is the identity.  #
-#                                                                             #
-#   When training a GP, the hyperparameters of its kernel (or covariance      #
-#   function) are optimized with respect to the given input-output pairs.     #
-#   When training a separate GP for each output component (as is usually      #
-#   done for multidimensional output, rather than fitting one multi-output    #
-#   model), one assumes that their covariance functions are independent.      #
-#   The SVD transformation ensures that this is a valid assumption.           #
-#                                                                             #
-#   The decorrelation by the SVD is done automatically when instantiating     #
-#   a `GaussianProcess`, but the user can choose to have the `predict` function         #
-#   return its predictions in the original space (by setting                  #
-#   transform_to_real=true)                                                   # 
-#                                                                             #
-#   Training points: {(x_i, y_i)} (i=1,...,n), where x_i ∈ ℝ ² and y_i ∈ ℝ ²  #
-#   The y_i are assumed to be related to the x_i by:                          #
-#   y_i = G(x_i) + η, where G is a map from ℝ ² to ℝ ², and η is noise drawn  #
-#   from a 2D normal distribution with known mean μ and covariance Σ          #
-#   Two Gaussian Process models are fit, one that predicts y_i[1] from x_i    #
-#   and one that predicts y_i[2] from x_i.                                    #
-#                                                                             #
-#   The GaussianProcessEmulator module can be used as a standalone module to fit           #
-#   Gaussian Process regression models, but it was originally designed as     #
-#   the "Emulate" step of the "Calibrate-Emulate-Sample" framework            #
-#   developed at CliMA.                                                       #
-#   Further reading on Calibrate-Emulate-Sample:                              #
-#       Cleary et al., 2020                                                   #
-#       https://arxiv.org/abs/2001.03689                                      #
-#                                                                             #
-###############################################################################
-
-rng_seed = 41
-Random.seed!(rng_seed)
-
-gppackage = GPJL()
-pred_type = YType()
-
-# Generate training data (x-y pairs, where x ∈ ℝ ᵖ, y ∈ ℝ ᵈ)
-# x = [x1, x2]: inputs/predictors/features/parameters
-# y = [y1, y2]: outputs/predictands/targets/observables
-# The observables y are related to the parameters x by: 
-# y = G(x1, x2) + η, 
-# where G(x1, x2) := [sin(x1) + cos(x2), sin(x1) - cos(x2)], and η ~ N(0, Σ)
-n = 200  # number of training points
-p = 2   # input dim 
-d = 2   # output dim
-
-X = 2.0 * π * rand(p, n)
-
-# G(x1, x2)
-g1x = sin.(X[1, :]) .+ cos.(X[2, :])
-g2x = sin.(X[1, :]) .- cos.(X[2, :])
-gx = zeros(2,n)
-gx[1,:] = g1x
-gx[2,:] = g2x
-# Add noise η
-μ = zeros(d) 
-Σ = 0.1 * [[0.8, 0.2] [0.2, 0.5]] # d x d
-noise_samples = rand(MvNormal(μ, Σ), n)
-
-# y = G(x) + η
-Y = gx .+ noise_samples
-
-# Fit 2D Gaussian Process regression model
-# (To be precise: We fit two models, one that predicts y1 from x1 and x2, and 
-# one that predicts y2 from x1 and x2)
-# Setting GPkernel=nothing leads to the creation of a default squared 
-# exponential kernel. 
-# Setting noise_learn=true leads to the addition of white noise to the kernel, 
-# whose hyperparameter -- the signal variance, or "noise" -- will be learned 
-# in the training phase via an optimization procedure.
-# Because of the SVD transformation applied to the output, we expect the 
-# learned noise to be close to 1.
-iopairs = PairedDataContainer(X,Y,data_are_columns=true)
-@assert get_inputs(iopairs) == X
-@assert get_outputs(iopairs) == Y
-
-gpobj1 = GaussianProcess(iopairs, gppackage, GPkernel=nothing, obs_noise_cov=Σ,
-               normalized=true, noise_learn=true, prediction_type=pred_type)
-
-println("\n-----------")
-println("Results of training Gaussian Process models with noise_learn=true")
-println("-----------")
-
-println("\nKernel of the GP trained to predict y1 from x=(x1, x2):")
-println(gpobj1.models[1].kernel)
-println("Learned noise parameter, σ_1:")
-learned_σ_1 = sqrt(gpobj1.models[1].kernel.kright.σ2)
-println("σ_1 = $learned_σ_1")
-# Check if the learned noise is approximately 1
-@assert(isapprox(learned_σ_1, 1.0; atol=0.1))
-
-println("\nKernel of the GP trained to predict y2 from x=(x1, x2):")
-println(gpobj1.models[2].kernel)
-println("Learned noise parameter, σ_2:")
-learned_σ_2 = sqrt(gpobj1.models[2].kernel.kright.σ2)
-println("σ_2 = $learned_σ_2")
-# Check if the learned noise is approximately 1
-@assert(isapprox(learned_σ_2, 1.0; atol=0.1))
-println("------------------------------------------------------------------\n")
-
-# For comparison: When noise_learn is set to false, the observational noise
-# is set to 1.0 and is not learned/optimized during the training. But thanks
-# to the SVD, 1.0 is the correct value to use.
-gpobj2 = GaussianProcess(iopairs, gppackage, GPkernel=nothing, obs_noise_cov=Σ,
-               normalized=true, noise_learn=false, prediction_type=pred_type)
-
-println("\n-----------")
-println("Results of training Gaussian Process models with noise_learn=false")
-println("-----------")
-
-println("\nKernel of the GP trained to predict y1 from x=(x1, x2):")
-# Note: In contrast to the kernels of the gpobj1 models, these ones do not
-# have a white noise ("Noise") kernel component
-println(gpobj2.models[1].kernel) 
-# logNoise is given as log(sqrt(noise))
-obs_noise_1 = exp(gpobj2.models[1].logNoise.value^2)
-println("Observational noise: $obs_noise_1")
-
-println("\nKernel of the GP trained to predict y1 from x=(x1, x2):")
-println(gpobj2.models[2].kernel)
-# logNoise is given as log(sqrt(noise))
-obs_noise_2 = exp(gpobj2.models[2].logNoise.value^2)
-println("Observational noise: $obs_noise_2")
-println("------------------------------------------------------------------")
diff --git a/src/CalibrateEmulateSample.jl b/src/CalibrateEmulateSample.jl
index dd7e014dc..3bda3f7c1 100644
--- a/src/CalibrateEmulateSample.jl
+++ b/src/CalibrateEmulateSample.jl
@@ -14,7 +14,8 @@ export EnsembleKalmanProcessModule,
        DataStorage
 
 # No internal deps, heavy external deps
-include("GaussianProcessEmulator.jl")
+#include("GaussianProcessEmulator.jl")
+include("Emulator.jl")
 
 # Internal deps, light external deps
 include("Utilities.jl")
diff --git a/src/Emulator.jl b/src/Emulator.jl
new file mode 100644
index 000000000..fd82462b4
--- /dev/null
+++ b/src/Emulator.jl
@@ -0,0 +1,413 @@
+module Emulators
+
+using ..DataStorage
+using Statistics
+using Distributions
+using LinearAlgebra
+using DocStringExtensions
+
+export Emulator
+export Decomposition
+
+export optimize_hyperparameters!
+export predict
+
+# SVD decomposition structure
+"""
+    Decomposition{<:AbstractFloat, <:Int}
+
+Struct of SVD decomposition, containing (V,S,Vt), and the size of S, N. 
+"""
+struct Decomposition{FT<:AbstractFloat, IT<:Int}
+    V::Array{FT,2}
+    Vt::Array{FT,2}
+    S::Array{FT}
+    N::IT
+end
+
+# SVD decomposition constructor
+function Decomposition(svd::SVD)
+	# svd.V is of type adjoint, transformed to Array with [:,:]
+	return Decomposition(svd.V[:,:], svd.Vt, svd.S, size(svd.S)[1])
+end
+
+"""
+    MachineLearningTool
+
+Type to dispatch different emulators:
+
+ - GaussianProcess <: MachineLearningTool
+"""
+abstract type MachineLearningTool end
+# defaults in error, all MachineLearningTools require these functions.
+function throw_define_mlt()
+    throw(ErrorException("Unknown MachineLearningTool defined, please use a known implementation"))
+end
+function build_models!(mlt,iopairs) throw_define_mlt() end
+function optimize_hyperparameters!(mlt) throw_define_mlt() end
+function predict(mlt,new_inputs) throw_define_mlt() end
+
+# include the different <: ML models
+include("GaussianProcess.jl") #for GaussianProcess
+# include("RandomFeature.jl")
+# include("NeuralNetwork.jl")
+# etc.
+
+
+# We will define the different emulator types after the general statements
+
+"""
+    Emulator
+
+Structure used to represent a general emulator:
+"""
+struct Emulator{FT}
+    "Machine learning tool, defined as a struct of type MachineLearningTool"
+    machine_learning_tool::MachineLearningTool
+     "normalized, standardized, transformed pairs given the Boolean's normalize_inputs, standardize_outputs, truncate_svd "
+    training_pairs::PairedDataContainer{FT}
+    "mean of input; 1 × input_dim"
+    input_mean::Array{FT}
+    "square root of the inverse of the input covariance matrix; input_dim × input_dim"
+    normalize_inputs::Bool
+    "whether to fit models on normalized outputs outputs / standardize_outputs_factor"
+    sqrt_inv_input_cov::Union{Nothing, Array{FT, 2}}
+    " if normalizing: whether to fit models on normalized inputs ((inputs - input_mean) * sqrt_inv_input_cov)"
+    standardize_outputs::Bool
+    "if standardizing: Standardization factors (characteristic values of the problem)"
+    standardize_outputs_factors
+    "the singular value decomposition of obs_noise_cov, such that obs_noise_cov = decomposition.U * Diagonal(decomposition.S) * decomposition.Vt. NB: the svd may be reduced in dimensions"
+    decomposition::Union{Decomposition, Nothing}
+end
+
+# Constructor for the Emulator Object
+function Emulator(
+    machine_learning_tool::MachineLearningTool,
+    input_output_pairs::PairedDataContainer{FT};
+    obs_noise_cov=nothing,
+    normalize_inputs::Bool = true,
+    standardize_outputs::Bool = false,
+    standardize_outputs_factors::Union{Array{FT,1}, Nothing}=nothing,
+    truncate_svd::FT=1.0
+    ) where {FT<:AbstractFloat}
+
+    # For Consistency checks
+    input_dim, output_dim = size(input_output_pairs, 1)
+    if obs_noise_cov != nothing
+        err2 = "obs_noise_cov must be of size ($output_dim, $output_dim), got $(size(obs_noise_cov))"
+        size(obs_noise_cov) == (output_dim, output_dim) || throw(ArgumentError(err2))
+    end
+
+    
+    # [1.] Normalize the inputs? 
+    input_mean = reshape(mean(get_inputs(input_output_pairs), dims=2), :, 1) #column vector
+    sqrt_inv_input_cov = nothing
+    if normalize_inputs
+        # Normalize (NB the inputs have to be of) size [input_dim × N_samples] to pass to GPE())
+        sqrt_inv_input_cov = sqrt(inv(Symmetric(cov(get_inputs(input_output_pairs), dims=2))))
+        training_inputs = normalize(
+            get_inputs(input_output_pairs),
+            input_mean,
+            sqrt_inv_input_cov
+        )
+    else
+        training_inputs = get_inputs(input_output_pairs)
+    end
+
+    # [2.] Standardize the outputs?
+    if standardize_outputs
+        training_outputs, obs_noise_cov = standardize(
+            get_outputs(input_output_pairs),
+            obs_noise_cov,
+            standardize_outputs_factors
+        )
+    else
+	training_outputs = get_outputs(input_output_pairs)
+    end
+
+    # [3.] Decorrelating the outputs [always performed]
+
+    #Transform data if obs_noise_cov available 
+    # (if obs_noise_cov==nothing, transformed_data is equal to data)
+    decorrelated_training_outputs, decomposition = svd_transform(training_outputs, obs_noise_cov, truncate_svd=truncate_svd)
+
+    # write new pairs structure 
+    if truncate_svd<1.0
+        #note this changes the dimension of the outputs
+        training_pairs = PairedDataContainer(training_inputs, decorrelated_training_outputs)
+        input_dim, output_dim = size(training_pairs, 1)
+    else
+        training_pairs = PairedDataContainer(training_inputs, decorrelated_training_outputs)
+    end
+
+    # [4.] build an emulator
+    build_models!(machine_learning_tool,training_pairs)
+    
+    return Emulator{FT}(machine_learning_tool,
+                        training_pairs,
+                        input_mean,
+                        normalize_inputs,
+                        sqrt_inv_input_cov,
+                        standardize_outputs,
+                        standardize_outputs_factors,
+                        decomposition)
+end    
+
+"""
+    function optimize_hyperparameters!(emulator::Emulator)
+
+optimize the hyperparameters in the machine learning tool
+"""
+function optimize_hyperparameters!(emulator::Emulator{FT}) where {FT}
+    optimize_hyperparameters!(emulator.machine_learning_tool)
+end
+
+
+"""
+    function predict(emulator::Emulator, new_inputs; transform_to_real=false) 
+
+makes a prediction using the emulator on new inputs (each new inputs given as data columns), default is to predict in the decorrelated space
+"""
+function predict(emulator::Emulator{FT}, new_inputs; transform_to_real=false) where {FT}
+
+    # Check if the size of new_inputs is consistent with the GP model's input
+    # dimension. 
+    input_dim, output_dim = size(emulator.training_pairs, 1)
+
+    N_samples = size(new_inputs, 2)
+    size(new_inputs, 1) == input_dim || throw(ArgumentError("GP object and input observations do not have consistent dimensions"))
+
+    # [1.] normalize
+    normalized_new_inputs = normalize(emulator,new_inputs)
+
+    # [2.]  predict. Note: ds = decorrelated, standard
+    ds_outputs, ds_output_var = predict(emulator.machine_learning_tool, normalized_new_inputs)
+
+    # [3.] transform back to real coordinates or remain in decorrelated coordinates
+    if transform_to_real && emulator.decomposition == nothing
+        throw(ArgumentError("""Need SVD decomposition to transform back to original space, 
+                 but GaussianProcess.decomposition == nothing. 
+                 Try setting transform_to_real=false"""))
+    elseif transform_to_real && emulator.decomposition != nothing
+        #transform back to real coords - cov becomes dense
+        s_outputs, s_output_cov = svd_reverse_transform_mean_cov(
+            ds_outputs, ds_output_var, emulator.decomposition)
+
+        if output_dim == 1
+            s_output_cov = [s_output_cov[i][1] for i in 1:N_samples]
+        end
+        # [4.] unstandardize
+        return reverse_standardize(emulator, s_outputs, s_output_cov)
+        
+    else
+        # remain in decorrelated, standardized coordinates (cov remains diagonal)
+        # Convert to vector of  matrices to match the format  
+        # when transform_to_real=true
+        ds_output_diagvar = vec([Diagonal(ds_output_var[:, j]) for j in 1:N_samples])
+        if output_dim == 1
+            ds_output_diagvar = [ds_output_diagvar[i][1] for i in 1:N_samples]
+        end
+
+        return ds_outputs, ds_output_diagvar
+        
+    end
+
+end
+
+
+
+
+# Normalization, Standardization, and Decorrelation
+"""
+    function normalize(emulator::Emulator, inputs)
+
+normalize the input data, with a normalizing function
+"""
+function normalize(emulator::Emulator{FT}, inputs) where {FT}
+    if emulator.normalize_inputs
+        return normalize(inputs, emulator.input_mean, emulator.sqrt_inv_input_cov)
+    else
+        return inputs
+    end
+end
+
+"""
+    function normalize(inputs, input_mean, sqrt_inv_input_cov)
+
+normalize with the empirical Gaussian distribution of points
+"""
+function normalize(inputs, input_mean, sqrt_inv_input_cov)
+    training_inputs = sqrt_inv_input_cov * (inputs .- input_mean)
+    return training_inputs 
+end
+
+"""
+    function standardize(outputs, output_cov, factors) 
+
+standardize with a vector of factors (size equal to output dimension)
+"""
+function standardize(outputs, output_cov, factors) 
+    standardized_outputs = outputs ./ factors
+    standardized_cov = output_cov ./ (factors .* factors')
+    return standardized_outputs, standardized_cov
+end
+
+"""
+    function reverse_standardize(emulator::Emulator, outputs, output_cov)
+
+reverse a previous standardization with the stored vector of factors (size equal to output dimension)
+"""
+function reverse_standardize(emulator::Emulator{FT}, outputs, output_cov) where {FT}
+    if emulator.standardize_outputs
+        return standardize(outputs, output_cov, 1. / emulator.standardize_output_factors)
+    else
+        return outputs, output_cov
+    end
+end
+
+
+
+"""
+svd_transform(data::Array{FT, 2}, obs_noise_cov::Union{Array{FT, 2}, Nothing}) where {FT}
+
+Apply a singular value decomposition (SVD) to the data
+  - `data` - GP training data/targets; output_dim × N_samples
+  - `obs_noise_cov` - covariance of observational noise
+
+Returns the transformed data and the decomposition, which is a matrix 
+factorization of type LinearAlgebra.SVD. 
+  
+Note: If F::SVD is the factorization object, U, S, V and Vt can be obtained via 
+F.U, F.S, F.V and F.Vt, such that A = U * Diagonal(S) * Vt. The singular values 
+in S are sorted in descending order.
+"""
+function svd_transform(
+    data::Array{FT, 2},
+    obs_noise_cov; 
+    truncate_svd::FT=1.0) where {FT}
+
+    if obs_noise_cov != nothing
+        @assert ndims(obs_noise_cov) == 2
+    end
+    if obs_noise_cov != nothing
+        # Truncate the SVD as a form of regularization
+	if truncate_svd<1.0 # this variable needs to be provided to this function
+            # Perform SVD
+            decomposition = svd(obs_noise_cov)
+            # Find cutoff
+            σ = decomposition.S
+            σ_cumsum = cumsum(σ) / sum(σ);
+            P_cutoff = truncate_svd;
+            ind = findall(x->x>P_cutoff, σ_cumsum); k = ind[1]
+            println("SVD truncated at k:")
+            println(k)
+            # Apply truncated SVD
+            n = size(obs_noise_cov)[1]
+            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S))
+	    transformed_data = sqrt_singular_values_inv[1:k,1:k] * decomposition.Vt[1:k,:] * data
+            transformed_data = transformed_data;
+            decomposition = Decomposition(decomposition.V[:,1:k], decomposition.Vt[1:k,:], 
+                                   decomposition.S[1:k], n)
+	else
+            decomposition = svd(obs_noise_cov)
+            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S)) 
+            transformed_data = sqrt_singular_values_inv * decomposition.Vt * data
+	    decomposition = Decomposition(svd(obs_noise_cov))
+        end
+    else
+        decomposition = nothing
+        transformed_data = data
+    end
+
+    return transformed_data, decomposition
+end
+
+
+"""
+function svd_transform(
+    data::Vector{FT}, 
+    obs_noise_cov::Union{Array{FT, 2}, Nothing};
+    truncate_svd::FT=1.0) where {FT}
+
+"""
+function svd_transform(
+    data::Vector{FT}, 
+    obs_noise_cov;
+    truncate_svd::FT=1.0) where {FT}
+
+   
+    if obs_noise_cov != nothing
+        @assert ndims(obs_noise_cov) == 2
+    end
+    if obs_noise_cov != nothing
+        # Truncate the SVD as a form of regularization
+	if truncate_svd<1.0 # this variable needs to be provided to this function
+            # Perform SVD
+            decomposition = svd(obs_noise_cov)
+            # Find cutoff
+            σ = decomposition.S
+            σ_cumsum = cumsum(σ) / sum(σ);
+            P_cutoff = truncate_svd;
+            ind = findall(x->x>P_cutoff, σ_cumsum); k = ind[1]
+            println("SVD truncated at k:")
+            println(k)
+            # Apply truncated SVD
+            n = size(obs_noise_cov)[1]
+            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S))
+	    transformed_data = sqrt_singular_values_inv[1:k,1:k] * decomposition.Vt[1:k,:] * data
+            transformed_data = transformed_data;
+            decomposition = Decomposition(decomposition.V[:,1:k], decomposition.Vt[1:k,:], 
+                                   decomposition.S[1:k], n)
+	else
+            decomposition = svd(obs_noise_cov)
+            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S)) 
+            transformed_data = sqrt_singular_values_inv * decomposition.Vt * data
+	    decomposition = Decomposition(svd(obs_noise_cov))
+        end
+    else
+        decomposition = nothing
+        transformed_data = data
+    end
+
+    return transformed_data, decomposition
+end
+
+"""
+svd_reverse_transform_mean_cov(μ::Array{FT, 2}, σ2::{Array{FT, 2}, decomposition::SVD) where {FT}
+
+Transform the mean and covariance back to the original (correlated) coordinate system
+  - `μ` - predicted mean; output_dim × N_predicted_points
+  - `σ2` - predicted variance; output_dim × N_predicted_points 
+
+Returns the transformed mean (output_dim × N_predicted_points) and variance. 
+Note that transforming the variance back to the original coordinate system
+results in non-zero off-diagonal elements, so instead of just returning the 
+elements on the main diagonal (i.e., the variances), we return the full 
+covariance at each point, as a vector of length N_predicted_points, where 
+each element is a matrix of size output_dim × output_dim
+"""
+function svd_reverse_transform_mean_cov(μ, σ2, 
+                                        decomposition::Union{SVD, Decomposition};
+					truncate_svd::FT=1.0) where {FT}
+    @assert ndims(μ) == 2
+    @assert ndims(σ2) == 2
+    
+    output_dim, N_predicted_points = size(σ2)
+    # We created meanvGP = D_inv * Vt * mean_v so meanv = V * D * meanvGP
+    sqrt_singular_values= Diagonal(sqrt.(decomposition.S))
+    transformed_μ = decomposition.V * sqrt_singular_values * μ
+    
+    transformed_σ2 = [zeros(output_dim, output_dim) for i in 1:N_predicted_points]
+    # Back transformation
+    
+    for j in 1:N_predicted_points
+        σ2_j = decomposition.V * sqrt_singular_values * Diagonal(σ2[:,j]) * sqrt_singular_values * decomposition.Vt
+        transformed_σ2[j] = σ2_j
+    end
+    
+    return transformed_μ, transformed_σ2
+end
+
+
+
+end
diff --git a/src/GaussianProcess.jl b/src/GaussianProcess.jl
new file mode 100644
index 000000000..3f5d7cdce
--- /dev/null
+++ b/src/GaussianProcess.jl
@@ -0,0 +1,319 @@
+
+using GaussianProcesses
+   
+using PyCall
+using ScikitLearn
+const pykernels = PyNULL()
+const pyGP = PyNULL()
+function __init__()
+    copy!(pykernels, pyimport("sklearn.gaussian_process.kernels"))
+    copy!(pyGP, pyimport("sklearn.gaussian_process"))
+end
+
+#exports (from Emulator)
+export GaussianProcess
+
+export GPJL, SKLJL
+export YType, FType
+
+"""
+    GaussianProcessesPackage
+
+Type to dispatch which GP package to use
+
+ - `GPJL` for GaussianProcesses.jl,
+ - `SKLJL` for the ScikitLearn GaussianProcessRegressor
+"""
+abstract type GaussianProcessesPackage end
+struct GPJL <: GaussianProcessesPackage end
+struct SKLJL <: GaussianProcessesPackage end
+
+"""
+    PredictionType
+
+Predict type for `GPJL` in GaussianProcesses.jl
+ - `YType`
+ - `FType` latent function
+"""
+abstract type PredictionType end
+struct YType <: PredictionType end
+struct FType <: PredictionType end
+
+"""
+    GaussianProcess{FT<:AbstractFloat} <: MachineLearningTool
+
+    Structure holding training input and the fitted Gaussian Process Regression
+models.
+
+# Fields
+$(DocStringExtensions.FIELDS)
+
+"""
+struct GaussianProcess{GPPackage} <: MachineLearningTool
+    "the Gaussian Process (GP) Regression model(s) that are fitted to the given input-data pairs"
+    models::Vector
+    "Kernel object"
+    kernel
+    "learn the noise with the White Noise kernel explicitly?"
+    noise_learn
+    "prediction type (`y` to predict the data, `f` to predict the latent function)"
+    prediction_type::Union{Nothing, PredictionType}
+end
+
+
+"""
+    function GaussianProcess(
+    package;
+    kernel::Union{K, KPy, Nothing}=nothing,
+    noise_learn=true,
+    prediction_type::PredictionType=YType(),
+    ) where {K<:Kernel, KPy<:PyObject}
+
+ - `package` - GaussianProcessPackage object
+ - `kernel` - GaussianProcesses kernel object. default is a Squared Exponential kernel.
+ - `noise_learn` - boolean to additionally learn white noise in decorrelated space. default is true.
+ - `prediction_type` - PredictionType object. default predicts data, not latent function (FType()).
+"""
+function GaussianProcess(
+    package;
+    kernel::Union{K, KPy, Nothing}=nothing,
+    noise_learn=true,
+    prediction_type::PredictionType=YType(),
+    ) where {K<:Kernel, KPy<:PyObject}
+
+    # Initialize vector for GP models
+    models = Any[]
+    
+    return GaussianProcess{typeof(package)}(models, kernel, noise_learn, prediction_type)
+			
+end
+
+# First we create  the GPJL implementation
+"""
+    function build_models!(
+        gp::GaussianProcess{package},
+        input_output_pairs)
+
+method to build gaussian process models based on the package
+"""
+function build_models!(
+    gp::GaussianProcess{GPJL},
+    input_output_pairs)
+    
+    # get inputs and outputs 
+    input_values = get_inputs(input_output_pairs)
+    output_values = get_outputs(input_output_pairs)
+
+    # Number of models (We are fitting one model per output dimension, as data is decorrelated)
+    models = gp.models
+    N_models = size(output_values,1); #size(transformed_data)[1]
+
+
+    # Use a default kernel unless a kernel was supplied to GaussianProcess
+    if gp.kernel==nothing
+        println("Using default squared exponential kernel, learning length scale and variance parameters")
+        # Construct kernel:
+        # Note that the kernels take the signal standard deviations on a
+        # log scale as input.
+        rbf_len = log.(ones(size(input_values, 1)))
+        rbf_logstd = log(1.0)
+        rbf = SEArd(rbf_len, rbf_logstd)
+        kern = rbf
+        println("Using default squared exponential kernel: ", kern)
+    else
+        kern = deepcopy(gp.kernel)
+        println("Using user-defined kernel", kern)
+    end
+
+    if gp.noise_learn
+        # Add white noise to kernel
+        white_logstd = log(1.0)
+        white = Noise(white_logstd)
+        kern = kern + white
+        println("Learning additive white noise")
+        
+        # Make the regularization small. We actually learn 
+        # total_noise = white_logstd + logstd_regularization_noise
+        magic_number = 1e-3 # magic_number << 1
+        logstd_regularization_noise = log(sqrt(magic_number)) 
+
+    else
+        # When not learning noise, our SVD transformation implies the 
+        # observational noise is the identity.
+        logstd_regularization_noise = log(sqrt(1.0)) 
+    end
+    
+    for i in 1:N_models
+        # Make a copy of the kernel (because it gets altered in every 
+        # iteration)
+        kernel_i = deepcopy(kern)
+        println("kernel in GaussianProcess:")
+        println(kernel_i)
+        data_i = output_values[i,:]
+        # GPE() arguments:
+        # input_values:    (input_dim × N_samples)
+        # GPdata_i:    (N_samples,)
+       
+        # Zero mean function
+        kmean = MeanZero()
+
+        # Instantiate GP model
+        m = GPE(input_values,
+                output_values[i,:],
+                kmean,
+                kernel_i, 
+                logstd_regularization_noise)
+       
+        println("created GP: ", i)
+        push!(models, m)
+        
+    end
+
+end
+
+"""
+    function optimize_hyperparameters!(gp::GaussianProcess{package})
+
+optimize Gaussian Process hyperparameters using in-build package method
+"""
+function optimize_hyperparameters!(gp::GaussianProcess{GPJL})
+    N_models = length(gp.models)
+    for i = 1:N_models
+        #noise_learn == true means we do it explicitly (thus don't need it here)
+        optimize!(gp.models[i], noise=!gp.noise_learn)
+        println("optimized hyperparameters of GP: ", i)
+        println(gp.models[i].kernel)
+    end
+end
+
+"""
+    function predict(gp::GaussianProcess{package}, new_inputs::Array{FT, 2})
+
+predict means and covariances in decorrelated output space using gaussian process models
+"""
+predict(gp::GaussianProcess{GPJL}, new_inputs::Array{FT, 2}) where {FT} = predict(gp, new_inputs, gp.prediction_type)
+
+function predict(gp::GaussianProcess{GPJL}, new_inputs::Array{FT, 2}, ::YType) where {FT}
+
+
+    M = length(gp.models)
+    N_samples = size(new_inputs,2)
+    # Predicts columns of inputs: input_dim × N_samples
+    μ = zeros(M, N_samples)
+    σ2 = zeros(M, N_samples)
+    for i in 1:M
+        μ[i,:],σ2[i,:] = predict_y(gp.models[i], new_inputs)
+    end
+
+    return μ, σ2
+end
+
+
+
+function predict(gp::GaussianProcess{GPJL}, new_inputs::Array{FT, 2}, ::FType) where {FT}
+
+    M = length(gp.models)
+    N_samples = size(new_inputs,2)
+    # Predicts columns of inputs: input_dim × N_samples
+    μ = zeros(M, N_samples)
+    σ2 = zeros(M, N_samples)
+    for i in 1:M
+        μ[i,:],σ2[i,:] = predict_f(gp.models[i], new_inputs)
+    end
+
+    return μ, σ2
+end
+
+
+
+
+#now we build the SKLJL implementation
+function build_models!(
+    gp::GaussianProcess{SKLJL},
+    input_output_pairs)
+
+      # get inputs and outputs 
+    input_values = permutedims(get_inputs(input_output_pairs), (2,1))
+    output_values = get_outputs(input_output_pairs)
+
+    # Number of models (We are fitting one model per output dimension, as data is decorrelated)
+    models = gp.models
+    N_models = size(output_values,1); #size(transformed_data)[1]
+
+    if gp.kernel==nothing
+        println("Using default squared exponential kernel, learning length scale and variance parameters")
+        # Create default squared exponential kernel
+        const_value = 1.0
+        var_kern = pykernels.ConstantKernel(constant_value=const_value,
+                                            constant_value_bounds=(1e-5, 1e4))
+        rbf_len = ones(size(input_values, 2))
+        rbf = pykernels.RBF(length_scale=rbf_len, 
+                            length_scale_bounds=(1e-5, 1e4))
+        kern = var_kern * rbf
+        println("Using default squared exponential kernel:", kern)
+    else
+        kern = deepcopy(gp.kernel)
+        println("Using user-defined kernel", kern)
+    end
+
+    if gp.noise_learn
+        # Add white noise to kernel
+        white_noise_level = 1.0
+        white = pykernels.WhiteKernel(noise_level=white_noise_level,
+                                      noise_level_bounds=(1e-05, 10.0))
+        kern = kern + white
+        println("Learning additive white noise")
+
+        # Make the regularization small. We actually learn 
+        # total_noise = white_noise_level + regularization_noise
+        magic_number = 1e-3 # magic_number << 1
+        regularization_noise = 1e-3
+    else
+        # When not learning noise, our SVD transformation implies the 
+        # observational noise is the identity.
+        regularization_noise = 1.0
+    end
+    
+    for i in 1:N_models
+        kernel_i = deepcopy(kern)
+        data_i = output_values[i, :]
+        m = pyGP.GaussianProcessRegressor(kernel=kernel_i,
+                                          n_restarts_optimizer=10,
+                                          alpha=regularization_noise)
+
+        # ScikitLearn.fit! arguments:
+        # input_values:    (N_samples × input_dim)
+        # data_i:    (N_samples,)
+        ScikitLearn.fit!(m, input_values, data_i)
+        if i==1
+            println(m.kernel.hyperparameters)
+            print("Completed training of: ")
+        end
+        println(i, ", ")
+        push!(models, m)
+        println(m.kernel)
+    end
+end
+
+
+function optimize_hyperparameters!(gp::GaussianProcess{SKLJL})
+    println("SKlearn, already trained. continuing...")
+end
+
+
+function predict(gp::GaussianProcess{SKLJL}, new_inputs::Array{FT, 2}) where {FT}
+
+    M = length(gp.models)
+    N_samples = size(new_inputs,2)
+    
+    # SKJL based on rows not columns; need to transpose inputs
+    μ = zeros(M, N_samples)
+    σ = zeros(M, N_samples)
+    for i in 1:M
+        μ[i,:],σ[i,:] = gp.models[i].predict(new_inputs', return_std=true)
+    end
+    σ2 = σ .* σ
+
+    return μ, σ2
+end
+
diff --git a/src/GaussianProcessEmulator.jl b/src/GaussianProcessEmulator.jl
deleted file mode 100644
index ab567b1b9..000000000
--- a/src/GaussianProcessEmulator.jl
+++ /dev/null
@@ -1,622 +0,0 @@
-module GaussianProcessEmulator
-
-using Statistics
-using Distributions
-using LinearAlgebra
-using GaussianProcesses
-using DocStringExtensions
-using ..DataStorage
-    
-using PyCall
-using ScikitLearn
-const pykernels = PyNULL()
-const pyGP = PyNULL()
-function __init__()
-    copy!(pykernels, pyimport("sklearn.gaussian_process.kernels"))
-    copy!(pyGP, pyimport("sklearn.gaussian_process"))
-end
-
-export GaussianProcess
-export predict
-
-export GPJL, SKLJL
-export YType, FType
-export svd_transform, svd_reverse_transform_mean_cov
-export decomp_struct
-
-"""
-    GaussianProcessesPackage
-
-Type to dispatch which GP package to use
-
- - `GPJL` for GaussianProcesses.jl,
- - `SKLJL` for the ScikitLearn GaussianProcessRegressor
-"""
-abstract type GaussianProcessesPackage end
-struct GPJL <: GaussianProcessesPackage end
-struct SKLJL <: GaussianProcessesPackage end
-
-"""
-    PredictionType
-
-Predict type for `GPJL` in GaussianProcesses.jl
- - `YType`
- - `FType` latent function
-"""
-abstract type PredictionType end
-struct YType <: PredictionType end
-struct FType <: PredictionType end
-
-
-# SVD decomposition structure
-struct decomp_struct{FT<:AbstractFloat, IT<:Int}
-    V::Array{FT,2}
-    Vt::Array{FT,2}
-    S::Array{FT}
-    N::IT
-end
-# SVD decomposition constructor
-function decomp_struct(svd::SVD)
-	# svd.V is of type adjoint, transformed to Array with [:,:]
-	return decomp_struct(svd.V[:,:], svd.Vt, svd.S, size(svd.S)[1])
-end
-
-"""
-    GaussianProcess{FT<:AbstractFloat}
-
-    Structure holding training input and the fitted Gaussian Process Regression
-models.
-
-# Fields
-$(DocStringExtensions.FIELDS)
-
-"""
-struct GaussianProcess{FT<:AbstractFloat, GPM}
-    "training inputs and outputs, data stored in columns"
-    input_output_pairs::PairedDataContainer{FT}
-    "mean of input; 1 × input_dim"
-    input_mean::Array{FT}
-    "square root of the inverse of the input covariance matrix; input_dim × input_dim"
-    sqrt_inv_input_cov::Union{Nothing, Array{FT, 2}}
-    "the Gaussian Process (GP) Regression model(s) that are fitted to the given input-data pairs"
-    models::Vector
-    "the singular value decomposition of obs_noise_cov, such that obs_noise_cov = decomposition.U * Diagonal(decomposition.S) * decomposition.Vt."
-    decomposition::Union{decomp_struct, Nothing}
-    "whether to fit GP models on normalized inputs ((inputs - input_mean) * sqrt_inv_input_cov)"
-    normalized::Bool
-    "prediction type (`y` to predict the data, `f` to predict the latent function)"
-    prediction_type::Union{Nothing, PredictionType}
-    "Standardization factors (characteristic values of the problem)"
-    norm_factors::Union{Array{FT}, Nothing}
-end
-
-
-"""
-    GaussianProcess(input_output_pairs::PairedDataContainer{FT}, package::GPJL; GPkernel::Union{K, KPy, Nothing}=nothing, obs_noise_cov::Union{Array{FT, 2}, Nothing}=nothing, normalized::Bool=true, noise_learn::Bool=true, prediction_type::PredictionType=YType()) where {K<:Kernel, KPy<:PyObject}
-
-Input-output pairs in paired data storage, storing in/out_dim × N_samples
-
- - `GPkernel` - GaussianProcesses kernel object. If not supplied, a default Squared Exponential kernel is used.
-"""
-
-function GaussianProcess(
-    input_output_pairs::PairedDataContainer{FT},
-    package::GPJL;
-    GPkernel::Union{K, KPy, Nothing}=nothing,
-    obs_noise_cov::Union{Array{FT, 2}, Nothing}=nothing,
-    normalized::Bool=true,
-    noise_learn::Bool=true,
-    truncate_svd::FT=1.0,
-    standardize::Bool=false,
-    prediction_type::PredictionType=YType(),
-    norm_factor::Union{Array{FT,1}, Nothing}=nothing) where {FT<:AbstractFloat, K<:Kernel, KPy<:PyObject}
-
-    # Consistency checks
-    input_dim, output_dim = size(input_output_pairs, 1)
-    
-    if obs_noise_cov != nothing
-        err2 = "obs_noise_cov must be of size ($output_dim, $output_dim), got $(size(obs_noise_cov))"
-        size(obs_noise_cov) == (output_dim, output_dim) || throw(ArgumentError(err2))
-    end
-
-    # Initialize vector of GP models
-    models = Any[]
-   
-
-    # Can use the full time median or some user define function
-    if standardize
-	output_values = get_outputs(input_output_pairs) ./ norm_factor
-	obs_noise_cov = obs_noise_cov ./ (norm_factor .* norm_factor')
-    else
-	output_values = get_outputs(input_output_pairs)
-    end
-
-    # Normalize the inputs if normalized==true 
-    input_mean = reshape(mean(get_inputs(input_output_pairs), dims=2), :, 1) #column vector
-    sqrt_inv_input_cov = nothing
-    if normalized
-        # Normalize (NB the inputs have to be of) size [input_dim × N_samples] to pass to GPE())
-        sqrt_inv_input_cov = sqrt(inv(Symmetric(cov(get_inputs(input_output_pairs), dims=2))))
-        GPinputs = sqrt_inv_input_cov * (get_inputs(input_output_pairs).-input_mean)
-    else
-        GPinputs = get_inputs(input_output_pairs)
-    end
-    
-    
-    # Transform data if obs_noise_cov available 
-    # (if obs_noise_cov==nothing, transformed_data is equal to data)
-    transformed_data, decomposition = svd_transform(output_values, 
-						    obs_noise_cov, truncate_svd=truncate_svd)
-
-    # Overwrite the input-output pairs because of the size discrepency
-    if truncate_svd<1.0
-	# Overwrite the input_output_pairs structure
-	input_output_pairs_old = deepcopy(input_output_pairs)
-	input_output_pairs = PairedDataContainer(get_inputs(input_output_pairs_old),
-					      transformed_data, data_are_columns=true)
-        input_dim, output_dim = size(input_output_pairs, 1)
-    end
-
-    # Number of models (We are fitting one model per output dimension)
-    N_models = output_dim; #size(transformed_data)[1]
-    
-    # Use a default kernel unless a kernel was supplied to GaussianProcess
-    if GPkernel==nothing
-        println("Using default squared exponential kernel, learning length scale and variance parameters")
-        # Construct kernel:
-        # Note that the kernels take the signal standard deviations on a
-        # log scale as input.
-        rbf_len = log.(ones(size(GPinputs, 1)))
-        rbf_logstd = log(1.0)
-        rbf = SEArd(rbf_len, rbf_logstd)
-        kern = rbf
-        println("Using default squared exponential kernel: ", kern)
-    else
-        kern = deepcopy(GPkernel)
-        println("Using user-defined kernel", kern)
-    end
-
-    if noise_learn
-        # Add white noise to kernel
-        white_logstd = log(1.0)
-        white = Noise(white_logstd)
-        kern = kern + white
-        println("Learning additive white noise")
-        
-        # Make the regularization small. We actually learn 
-        # total_noise = white_logstd + logstd_regularization_noise
-        magic_number = 1e-3 # magic_number << 1
-        logstd_regularization_noise = log(sqrt(magic_number)) 
-
-    else
-        # When not learning noise, our SVD transformation implies the 
-        # observational noise is the identity.
-        logstd_regularization_noise = log(sqrt(1.0)) 
-    end
-
-    for i in 1:N_models
-        # Make a copy of the kernel (because it gets altered in every 
-        # iteration)
-        println("kernel in GaussianProcess")
-        GPkernel_i = deepcopy(kern)
-        println(GPkernel_i)
-        GPdata_i = transformed_data[i,:]
-        # GPE() arguments:
-        # GPinputs:    (input_dim × N_samples)
-        # GPdata_i:    (N_samples,)
-       
-        # Zero mean function
-        kmean = MeanZero()
-
-        # Instantiate GP model
-        m = GPE(GPinputs,
-                GPdata_i,
-                kmean,
-                GPkernel_i, 
-                logstd_regularization_noise)
-       
-        # We choose above to explicitly learn the WhiteKernel as opposed to 
-        # using the in built noise=true functionality.
-        println("created GP", i)
-        optimize!(m, noise=false)
-        println("optimized GP", i)
-        push!(models, m)
-        println(m.kernel)
-    end
-    
-    return GaussianProcess{FT,typeof(package)}(input_output_pairs,
-                                               input_mean, 
-                                               sqrt_inv_input_cov,
-                                               models,
-                                               decomposition,
-                                               normalized,
-                                               prediction_type,
-					       norm_factor)
-end
-
-
-"""
-    GaussianProcess(input_output_pairs::PairedDataContainer, package::SKLJL; GPkernel::Union{K, KPy, Nothing}=nothing, obs_noise_cov::Union{Array{FT, 2}, Nothing}=nothing, normalized::Bool=true, noise_learn::Bool=true, prediction_type::PredictionType=YType()) where {K<:Kernel, KPy<:PyObject}
-
-Input-output pairs in paired data storage, storing in/out_dim × N_samples
-
- - `GPkernel` - ScikitLearn kernel object. If not supplied, a default Squared Exponential kernel is used.
-"""
-function GaussianProcess(
-    input_output_pairs::PairedDataContainer{FT},
-    package::SKLJL;
-    GPkernel::Union{K, KPy, Nothing}=nothing,
-    obs_noise_cov::Union{Array{FT, 2}, Nothing}=nothing,
-    normalized::Bool=true,
-    noise_learn::Bool=true,
-    truncate_svd::FT=1.0,
-    standardize::Bool=false,
-    prediction_type::PredictionType=YType(),
-    norm_factor::Union{Array{FT,1}, Nothing}=nothing) where {FT<:AbstractFloat, K<:Kernel, KPy<:PyObject}
-
-    # Consistency checks
-    input_dim, output_dim = size(input_output_pairs, 1)
-
-    if obs_noise_cov != nothing
-        err2 = "obs_noise_cov must be of size ($output_dim, $output_dim), got $(size(obs_noise_cov))"
-        size(obs_noise_cov) == (output_dim, output_dim) || throw(ArgumentError(err2))
-    end
-
-    # Initialize vector of GP models
-    models = Any[]
-    # Number of models (We are fitting one model per output dimension)
-    N_models = output_dim
-
-    # Can use the full time median or some user define function
-    if standardize
-        #norm_factors = get_standarizing_factors() 
-	output_values = get_outputs(input_output_pairs) ./ norm_factor
-	obs_noise_cov = obs_noise_cov ./ (norm_factor .* norm_factor')
-    else
-	output_values = get_outputs(input_output_pairs)
-    end
-
-    
-    # Normalize the inputs if normalized==true
-    # Note that contrary to the GaussianProcesses.jl (GPJL) GPE, the 
-    # ScikitLearn (SKLJL) GaussianProcessRegressor requires inputs to be of 
-    # size N_samples × input_dim, so one needs to transpose
-    input_mean = reshape(mean(get_inputs(input_output_pairs), dims=2), :, 1)
-    sqrt_inv_input_cov = nothing
-    if normalized
-        sqrt_inv_input_cov = sqrt(inv(Symmetric(cov(get_inputs(input_output_pairs), dims=2))))
-        GPinputs = permutedims(sqrt_inv_input_cov*(get_inputs(input_output_pairs) .- input_mean), (2,1))
-    else
-        GPinputs = permutedims(get_inputs(input_output_pairs), (2,1))
-    end
-
-    # Transform data if obs_noise_cov available (if obs_noise_cov==nothing, 
-    # transformed_data is equal to data)
-    transformed_data, decomposition = svd_transform(output_values, 
-						    obs_noise_cov, truncate_svd=truncate_svd)
-
-    if GPkernel==nothing
-        println("Using default squared exponential kernel, learning length scale and variance parameters")
-        # Create default squared exponential kernel
-        const_value = 1.0
-        var_kern = pykernels.ConstantKernel(constant_value=const_value,
-                                            constant_value_bounds=(1e-5, 1e4))
-        rbf_len = ones(size(GPinputs, 2))
-        rbf = pykernels.RBF(length_scale=rbf_len, 
-                            length_scale_bounds=(1e-5, 1e4))
-        kern = var_kern * rbf
-        println("Using default squared exponential kernel:", kern)
-    else
-        kern = deepcopy(GPkernel)
-        println("Using user-defined kernel", kern)
-    end
-
-    if noise_learn
-        # Add white noise to kernel
-        white_noise_level = 1.0
-        white = pykernels.WhiteKernel(noise_level=white_noise_level,
-                                      noise_level_bounds=(1e-05, 10.0))
-        kern = kern + white
-        println("Learning additive white noise")
-
-        # Make the regularization small. We actually learn 
-        # total_noise = white_noise_level + regularization_noise
-        magic_number = 1e-3 # magic_number << 1
-        regularization_noise = 1e-3
-    else
-        # When not learning noise, our SVD transformation implies the 
-        # observational noise is the identity.
-        regularization_noise = 1.0
-    end
-    
-    for i in 1:N_models
-        GPkernel_i = deepcopy(kern)
-        GPdata_i = transformed_data[i, :]
-        m = pyGP.GaussianProcessRegressor(kernel=GPkernel_i,
-                                          n_restarts_optimizer=10,
-                                          alpha=regularization_noise)
-
-        # ScikitLearn.fit! arguments:
-        # GPinputs:    (N_samples × input_dim)
-        # GPdata_i:    (N_samples,)
-        ScikitLearn.fit!(m, GPinputs, GPdata_i)
-        if i==1
-            println(m.kernel.hyperparameters)
-            print("Completed training of: ")
-        end
-        println(i, ", ")
-        push!(models, m)
-        println(m.kernel)
-    end
-    return GaussianProcess{FT, typeof(package)}(input_output_pairs,
-                                                input_mean,
-                                                sqrt_inv_input_cov,
-                                                models,
-                                                decomposition,
-                                                normalized,
-                                                YType(),
-						norm_factor)
-end
-
-
-"""
-    predict(gp::GaussianProcess{FT, GPJL}, new_inputs::Array{FT, 2}, transform_to_real::Bool=false) where {FT} = predict(gp, new_inputs, gp.prediction_type)
-
-Evaluate the GP model(s) at new inputs.
-  - `gp` - a GaussianProcess
-  - `new_inputs` - inputs for which GP model(s) is/are evaluated; input_dim × N_samples
-
-Returns the predicted mean(s) and covariance(s) at the input points. 
-Means: matrix of size output_dim × N_samples 
-Covariances: vector of length N_samples, each element is a matrix of size output_dim × output_dim. If the output is 1-dimensional, a 1 × N_samples array of scalar variances is returned rather than a vector of 1x1 matrices.
-
-Note: If gp.normalized == true, the new inputs are normalized prior to the prediction
-"""
-predict(gp::GaussianProcess{FT, GPJL}, new_inputs::Array{FT, 2}; transform_to_real::Bool=false) where {FT} = predict(gp, new_inputs, transform_to_real, gp.prediction_type)
-
-function predict(gp::GaussianProcess{FT, GPJL}, new_inputs::Array{FT, 2}, transform_to_real, ::FType) where {FT}
-
-    # Check if the size of new_inputs is consistent with the GP model's input
-    # dimension. 
-    input_dim, output_dim = size(gp.input_output_pairs, 1)
-    N_samples = size(new_inputs, 2)
-    size(new_inputs, 1) == input_dim || throw(ArgumentError("GP object and input observations do not have consistent dimensions"))
-
-    if gp.normalized
-        new_inputs = gp.sqrt_inv_input_cov * (new_inputs .- gp.input_mean)  
-    end
-
-    M = length(gp.models)
-    # Predicts columns of inputs: input_dim × N_samples
-    μ = zeros(output_dim,N_samples)
-    σ2 = zeros(output_dim,N_samples)
-    for i in 1:M
-        μ[i,:],σ2[i,:] = predict_f(gp.models[i], new_inputs)
-    end
-
-    if transform_to_real && gp.decomposition != nothing
-        μ_pred, σ2_pred = svd_reverse_transform_mean_cov(μ, σ2,
-                                                         gp.decomposition)
-    elseif transform_to_real && gp.decomposition == nothing
-        throw(ArgumentError("""Need SVD decomposition to transform back to original space, 
-                 but GaussianProcess.decomposition == nothing. 
-                 Try setting transform_to_real=false"""))
-    else
-        μ_pred = μ
-        # Convert to vector of (diagonal) matrices to match the format of 
-        # σ2_pred when transform_to_real=true
-        σ2_pred = vec([Diagonal(σ2[:, j]) for j in 1:N_samples])
-    end
-
-    if output_dim == 1
-        σ2_pred = [σ2_pred[i][1] for i in 1:N_samples]
-    end
-
-    return μ_pred, σ2_pred
-end
-
-function predict(gp::GaussianProcess{FT, GPJL}, new_inputs::Array{FT, 2}, transform_to_real, ::YType) where {FT}
-
-    # Check if the size of new_inputs is consistent with the GP model's input
-    # dimension. 
-    input_dim, output_dim = size(gp.input_output_pairs, 1)
-
-    N_samples = size(new_inputs, 2)
-    size(new_inputs, 1) == input_dim || throw(ArgumentError("GP object and input observations do not have consistent dimensions"))
-
-    if gp.normalized
-        new_inputs = gp.sqrt_inv_input_cov * (new_inputs .- gp.input_mean)  
-    end
-
-    M = length(gp.models)
-    # Predicts columns of inputs: input_dim × N_samples
-    μ = zeros(output_dim,N_samples)
-    σ2 = zeros(output_dim,N_samples)
-    for i in 1:M
-        μ[i,:],σ2[i,:] = predict_f(gp.models[i], new_inputs)
-    end
-    if transform_to_real && gp.decomposition != nothing
-        μ_pred, σ2_pred = svd_reverse_transform_mean_cov(μ, σ2, 
-                                                         gp.decomposition)
-    elseif transform_to_real && gp.decomposition == nothing
-        throw(ArgumentError("""Need SVD decomposition to transform back to original space, 
-                 but GaussianProcess.decomposition == nothing. 
-                 Try setting transform_to_real=false"""))
-    else
-        μ_pred = μ
-        # Convert to vector of (diagonal) matrices to match the format of 
-        # σ2_pred when transform_to_real=true
-        σ2_pred = vec([Diagonal(σ2[:, j]) for j in 1:N_samples])
-    end
-
-    if output_dim == 1
-        σ2_pred = [σ2_pred[i][1] for i in 1:N_samples]
-    end
-
-    return μ_pred, σ2_pred
-end
-
-function predict(gp::GaussianProcess{FT, SKLJL}, new_inputs::Array{FT, 2}, transform_to_real::Bool=false) where {FT}
-
-    # Check if the size of new_inputs is consistent with the GP model's input
-    # dimension. 
-    input_dim, output_dim = size(gp.input_output_pairs, 1)
-
-    N_samples = size(new_inputs, 2)
-    size(new_inputs, 1) == input_dim || throw(ArgumentError("GP object and input observations do not have consistent dimensions"))
-
-    if gp.normalized
-        new_inputs = gp.sqrt_inv_input_cov * (new_inputs .- gp.input_mean)  
-    end
-
-    M = length(gp.models)
-
-    # SKJL based on rows not columns; need to transpose inputs
-    μ = zeros(output_dim,N_samples)
-    σ = zeros(output_dim,N_samples)
-    for i in 1:M
-        μ[i,:],σ[i,:] = gp.models[i].predict(new_inputs', return_std=true)
-    end
-    σ2 = σ .* σ
-
-    if transform_to_real && gp.decomposition != nothing
-        μ_pred, σ2_pred = svd_reverse_transform_mean_cov(μ, σ2, 
-                                                         gp.decomposition)
-    elseif transform_to_real && gp.decomposition == nothing
-        throw(ArgumentError("""Need SVD decomposition to transform back to original space, 
-                 but GaussianProcess.decomposition == nothing. 
-                 Try setting transform_to_real=false"""))
-    else
-        μ_pred = μ
-        # Convert to vector of (diagonal) matrices to match the format of 
-        # σ2_pred when transform_to_real=true
-        σ2_pred = vec([Diagonal(σ2[:, j]) for j in 1:N_samples])
-    end
-
-    if output_dim == 1
-        σ2_pred = reshape([σ2_pred[i][1] for i in 1:N_samples], 1,N_samples)
-    end
-
-    return μ_pred, σ2_pred
-end
-
-"""
-svd_transform(data::Array{FT, 2}, obs_noise_cov::Union{Array{FT, 2}, Nothing}) where {FT}
-
-Apply a singular value decomposition (SVD) to the data
-  - `data` - GP training data/targets; output_dim × N_samples
-  - `obs_noise_cov` - covariance of observational noise
-
-Returns the transformed data and the decomposition, which is a matrix 
-factorization of type LinearAlgebra.SVD. 
-  
-Note: If F::SVD is the factorization object, U, S, V and Vt can be obtained via 
-F.U, F.S, F.V and F.Vt, such that A = U * Diagonal(S) * Vt. The singular values 
-in S are sorted in descending order.
-"""
-function svd_transform(data::Array{FT, 2}, obs_noise_cov::Union{Array{FT, 2}, Nothing}; 
-		       truncate_svd::FT=1.0) where {FT}
-    if obs_noise_cov != nothing
-        # Truncate the SVD as a form of regularization
-	if truncate_svd<1.0 # this variable needs to be provided to this function
-            # Perform SVD
-            decomposition = svd(obs_noise_cov)
-            # Find cutoff
-            σ = decomposition.S
-            σ_cumsum = cumsum(σ) / sum(σ);
-            P_cutoff = truncate_svd;
-            ind = findall(x->x>P_cutoff, σ_cumsum); k = ind[1]
-            println("SVD truncated at k:")
-            println(k)
-            # Apply truncated SVD
-            n = size(obs_noise_cov)[1]
-            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S))
-	    transformed_data = sqrt_singular_values_inv[1:k,1:k] * decomposition.Vt[1:k,:] * data
-            transformed_data = transformed_data;
-            decomposition = decomp_struct(decomposition.V[:,1:k], decomposition.Vt[1:k,:], 
-                                   decomposition.S[1:k], n)
-	else
-            decomposition = svd(obs_noise_cov)
-            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S)) 
-            transformed_data = sqrt_singular_values_inv * decomposition.Vt * data
-	    decomposition = decomp_struct(svd(obs_noise_cov))
-        end
-    else
-        decomposition = nothing
-        transformed_data = data
-    end
-
-    return transformed_data, decomposition
-end
-
-function svd_transform(data::Vector{FT}, 
-		       obs_noise_cov::Union{Array{FT, 2}, Nothing};
-		       truncate_svd::FT=1.0) where {FT}
-     if obs_noise_cov != nothing
-        # Truncate the SVD as a form of regularization
-	if truncate_svd<1.0 # this variable needs to be provided to this function
-            # Perform SVD
-            decomposition = svd(obs_noise_cov)
-            # Find cutoff
-            σ = decomposition.S
-            σ_cumsum = cumsum(σ) / sum(σ);
-            P_cutoff = truncate_svd;
-            ind = findall(x->x>P_cutoff, σ_cumsum); k = ind[1]
-            println("SVD truncated at k:")
-            println(k)
-            # Apply truncated SVD
-            n = size(obs_noise_cov)[1]
-            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S))
-	    transformed_data = sqrt_singular_values_inv[1:k,1:k] * decomposition.Vt[1:k,:] * data
-            transformed_data = transformed_data;
-            decomposition = decomp_struct(decomposition.V[:,1:k], decomposition.Vt[1:k,:], 
-                                   decomposition.S[1:k], n)
-	else
-            decomposition = svd(obs_noise_cov)
-            sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(decomposition.S)) 
-            transformed_data = sqrt_singular_values_inv * decomposition.Vt * data
-	    decomposition = decomp_struct(svd(obs_noise_cov))
-        end
-    else
-        decomposition = nothing
-        transformed_data = data
-    end
-
-    return transformed_data, decomposition
-end
-
-"""
-svd_reverse_transform_mean_cov(μ::Array{FT, 2}, σ2::{Array{FT, 2}, decomposition::SVD) where {FT}
-
-Transform the mean and covariance back to the original (correlated) coordinate system
-  - `μ` - predicted mean; output_dim × N_predicted_points
-  - `σ2` - predicted variance; output_dim × N_predicted_points 
-
-Returns the transformed mean (output_dim × N_predicted_points) and variance. 
-Note that transforming the variance back to the original coordinate system
-results in non-zero off-diagonal elements, so instead of just returning the 
-elements on the main diagonal (i.e., the variances), we return the full 
-covariance at each point, as a vector of length N_predicted_points, where 
-each element is a matrix of size output_dim × output_dim
-"""
-function svd_reverse_transform_mean_cov(μ::Array{FT, 2}, σ2::Array{FT, 2}, 
-                                        decomposition::Union{SVD, decomp_struct};
-					truncate_svd::FT=1.0) where {FT}
-        output_dim, N_predicted_points = size(σ2)
-        # We created meanvGP = D_inv * Vt * mean_v so meanv = V * D * meanvGP
-        sqrt_singular_values= Diagonal(sqrt.(decomposition.S))
-        transformed_μ = decomposition.V * sqrt_singular_values * μ
-
-        transformed_σ2 = [zeros(output_dim, output_dim) for i in 1:N_predicted_points]
-        # Back transformation
-
-        for j in 1:N_predicted_points
-            σ2_j = decomposition.V * sqrt_singular_values * Diagonal(σ2[:,j]) * sqrt_singular_values * decomposition.Vt
-            transformed_σ2[j] = σ2_j
-        end
-
-    return transformed_μ, transformed_σ2
-end
-
-
-end 
diff --git a/src/MarkovChainMonteCarlo.jl b/src/MarkovChainMonteCarlo.jl
index 64c54f7d1..6996a36f3 100644
--- a/src/MarkovChainMonteCarlo.jl
+++ b/src/MarkovChainMonteCarlo.jl
@@ -1,6 +1,6 @@
 module MarkovChainMonteCarlo
 
-using ..GaussianProcessEmulator
+using ..Emulators
 using ..ParameterDistributionStorage
 
 using Statistics
@@ -97,7 +97,7 @@ function MCMC(
     # We need to transform obs_sample into the correct space 
     if svdflag
         println("Applying SVD to decorrelating outputs, if not required set svdflag=false")
-        obs_sample, unused = svd_transform(obs_sample, obs_noise_cov; truncate_svd=truncate_svd)
+        obs_sample, unused = Emulators.svd_transform(obs_sample, obs_noise_cov; truncate_svd=truncate_svd)
     else
         println("Assuming independent outputs.")
     end
@@ -200,7 +200,7 @@ function log_likelihood(mcmc::MCMC{FT},
 	#full_cov = Diagonal(gvar)
 	eigs = eigvals(gcov)
 	log_gpfidelity = -FT(0.5) * sum(log.(eigs))
-	# Combine got log_rho
+    # Combine got log_rho
 	diff = g - mcmc.obs_sample
         log_rho[1] = -FT(0.5) * diff' * (gcov \ diff) + log_gpfidelity
     #end
@@ -226,7 +226,7 @@ function proposal(mcmc::MCMC)
 end
 
 
-function find_mcmc_step!(mcmc_test::MCMC{FT}, gp::GaussianProcess{FT}; max_iter=2000) where {FT}
+function find_mcmc_step!(mcmc_test::MCMC{FT}, em::Emulator{FT}; max_iter=2000) where {FT}
     step = mcmc_test.step[1]
     mcmc_accept = false
     doubled = false
@@ -241,11 +241,11 @@ function find_mcmc_step!(mcmc_test::MCMC{FT}, gp::GaussianProcess{FT}; max_iter=
     while mcmc_accept == false
 
         param = reshape(mcmc_test.param, :, 1)
-        gp_pred, gp_predvar = predict(gp, param )
-        if ndims(gp_predvar[1]) != 0
-            mcmc_sample!(mcmc_test, vec(gp_pred), diag(gp_predvar[1]))
+        em_pred, em_predvar = predict(em, param )
+        if ndims(em_predvar[1]) != 0
+            mcmc_sample!(mcmc_test, vec(em_pred), diag(em_predvar[1]))
         else
-            mcmc_sample!(mcmc_test, vec(gp_pred), vec(gp_predvar))
+            mcmc_sample!(mcmc_test, vec(em_pred), vec(em_predvar))
         end
         it += 1
         if it % max_iter == 0
@@ -289,18 +289,18 @@ end
 
 
 function sample_posterior!(mcmc::MCMC{FT,IT},
-                           gp::GaussianProcess{FT},
+                           em::Emulator{FT},
                            max_iter::IT) where {FT,IT<:Int}
 
     for mcmcit in 1:max_iter
         param = reshape(mcmc.param, :, 1)
         # test predictions (param is 1 x N_parameters)
-        gp_pred, gp_predvar = predict(gp, param)
+        em_pred, em_predvar = predict(em, param)
 
-        if ndims(gp_predvar[1]) != 0
-            mcmc_sample!(mcmc, vec(gp_pred), diag(gp_predvar[1]))
+        if ndims(em_predvar[1]) != 0
+            mcmc_sample!(mcmc, vec(em_pred), diag(em_predvar[1]))
         else
-            mcmc_sample!(mcmc, vec(gp_pred), vec(gp_predvar))
+            mcmc_sample!(mcmc, vec(em_pred), vec(em_predvar))
         end
 
     end
diff --git a/test/Emulator/runtests.jl b/test/Emulator/runtests.jl
new file mode 100644
index 000000000..3d754cb73
--- /dev/null
+++ b/test/Emulator/runtests.jl
@@ -0,0 +1,156 @@
+# Import modules
+using Random
+using Test
+using Statistics 
+using Distributions
+using LinearAlgebra
+
+using CalibrateEmulateSample.Emulators
+using CalibrateEmulateSample.DataStorage
+
+@testset "Emulators" begin
+
+   
+    #build some quick data + noise
+    m=50
+    d=6
+    x = rand(3, m) #R^3
+    y = rand(d, m) #R^5
+    
+    # "noise"
+    μ = zeros(d) 
+    Σ = rand(d,d) 
+    Σ = Σ'*Σ 
+    noise_samples = rand(MvNormal(μ, Σ), m)
+    y += noise_samples
+    
+    iopairs = PairedDataContainer(x,y,data_are_columns=true)
+    @test get_inputs(iopairs) == x
+    @test get_outputs(iopairs) == y
+
+    
+    
+    # [1.] test SVD (2D y version)
+    test_SVD = svd(Σ)
+    transformed_y, decomposition = Emulators.svd_transform(y, Σ, truncate_svd=1.0)
+    @test_throws AssertionError Emulators.svd_transform(y, Σ[:,1], truncate_svd=1.0)
+    @test test_SVD.V[:,:] == decomposition.V #(use [:,:] to make it an array)
+    @test test_SVD.Vt == decomposition.Vt
+    @test test_SVD.S == decomposition.S
+    @test size(test_SVD.S)[1] == decomposition.N
+    @test size(transformed_y) == size(y)
+
+    # 1D y version
+    transformed_y, decomposition2 = Emulators.svd_transform(y[:, 1], Σ, truncate_svd=1.0)
+    @test_throws AssertionError Emulators.svd_transform(y[:,1], Σ[:,1], truncate_svd=1.0)
+    @test size(transformed_y) == size(y[:, 1])
+    
+    # Reverse SVD
+    y_new, y_cov_new = Emulators.svd_reverse_transform_mean_cov(reshape(transformed_y,(d,1)),ones(d,1),decomposition2, truncate_svd=1.0)
+    @test_throws AssertionError Emulators.svd_reverse_transform_mean_cov(transformed_y,ones(d,1),decomposition2, truncate_svd=1.0)
+    @test_throws AssertionError Emulators.svd_reverse_transform_mean_cov(reshape(transformed_y,(d,1)),ones(d),decomposition2, truncate_svd=1.0)
+    @test size(y_new)[1] == size(y[:, 1])[1]
+    @test y_new ≈ y[:,1]
+    @test y_cov_new[1] ≈ Σ
+    
+    # Truncation
+    transformed_y, trunc_decomposition = Emulators.svd_transform(y[:, 1], Σ, truncate_svd=0.95)
+    trunc_size = size(trunc_decomposition.S)[1]
+    @test test_SVD.S[1:trunc_size] == trunc_decomposition.S
+    @test size(transformed_y)[1] == trunc_size
+    
+    # [2.] test Normalization
+    input_mean = reshape(mean(get_inputs(iopairs), dims=2), :, 1) #column vector
+    sqrt_inv_input_cov = sqrt(inv(Symmetric(cov(get_inputs(iopairs), dims=2))))
+
+    norm_inputs = Emulators.normalize(get_inputs(iopairs),input_mean,sqrt_inv_input_cov)
+    @test norm_inputs == sqrt_inv_input_cov * (get_inputs(iopairs) .- input_mean)
+
+    # [3.] test Standardization
+    norm_factors = 10.0
+    norm_factors = fill(norm_factors, size(y[:,1])) # must be size of output dim
+
+    s_y, s_y_cov = Emulators.standardize(get_outputs(iopairs),Σ,norm_factors)
+    @test s_y == get_outputs(iopairs)./norm_factors
+    @test s_y_cov == Σ ./ (norm_factors.*norm_factors')
+    
+    
+    
+    # [4.] test emulator preserves the structures
+
+    #build an unknown type
+    struct MLTester <: Emulators.MachineLearningTool end
+    
+    mlt = MLTester()
+    
+    @test_throws ErrorException emulator = Emulator(
+        mlt,
+        iopairs,
+        obs_noise_cov=Σ,
+        normalize_inputs=true,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+
+    #build a known type, with defaults
+    gp = GaussianProcess(GPJL())
+
+    emulator = Emulator(
+        gp,
+        iopairs,
+        obs_noise_cov=Σ,
+        normalize_inputs=false,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+    
+    # compare SVD/norm/stand with stored emulator version
+    test_decomp = emulator.decomposition
+    @test test_decomp.V == decomposition.V #(use [:,:] to make it an array)
+    @test test_decomp.Vt == decomposition.Vt
+    @test test_decomp.S == decomposition.S
+    @test test_decomp.N == decomposition.N
+
+    emulator2 = Emulator(
+        gp,
+        iopairs,
+        obs_noise_cov=Σ,
+        normalize_inputs=true,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+    train_inputs = get_inputs(emulator2.training_pairs)
+    @test norm_inputs == train_inputs
+
+    train_inputs2 = Emulators.normalize(emulator2,get_inputs(iopairs))
+    @test norm_inputs == train_inputs
+
+    # reverse standardise
+
+    emulator3 = Emulator(
+        gp,
+        iopairs,
+        obs_noise_cov=Σ,
+        normalize_inputs=false,
+        standardize_outputs=true,
+        standardize_outputs_factors=norm_factors,        
+        truncate_svd=1.0)
+
+    #standardized and decorrelated (sd) data
+    sd_train_outputs = get_outputs(emulator3.training_pairs)
+    sqrt_singular_values_inv = Diagonal(1.0 ./ sqrt.(emulator3.decomposition.S)) 
+    decorrelated_s_y = sqrt_singular_values_inv * emulator3.decomposition.Vt * s_y
+    @test decorrelated_s_y == sd_train_outputs
+
+
+    # truncation
+    emulator4 = Emulator(
+        gp,
+        iopairs,
+        obs_noise_cov=Σ,
+        normalize_inputs=false,
+        standardize_outputs=false,
+        truncate_svd=0.9)
+    trunc_size = size(emulator4.decomposition.S)[1]
+    @test test_SVD.S[1:trunc_size] == emulator4.decomposition.S
+
+    
+
+end
diff --git a/test/GaussianProcess/runtests.jl b/test/GaussianProcess/runtests.jl
new file mode 100644
index 000000000..5db2c1ab8
--- /dev/null
+++ b/test/GaussianProcess/runtests.jl
@@ -0,0 +1,199 @@
+# Import modules
+using Random
+using Test
+using GaussianProcesses
+using Statistics 
+using Distributions
+using ScikitLearn
+using LinearAlgebra
+@sk_import gaussian_process : GaussianProcessRegressor
+@sk_import gaussian_process.kernels : (RBF, WhiteKernel, ConstantKernel)
+
+using CalibrateEmulateSample.Emulators
+using CalibrateEmulateSample.DataStorage
+
+@testset "GaussianProcess" begin
+
+    # Seed for pseudo-random number generator
+    rng_seed = 41
+    Random.seed!(rng_seed)
+
+    # -------------------------------------------------------------------------
+    # Test case 1: 1D input, 1D output
+    # -------------------------------------------------------------------------
+
+    # Training data
+    n = 20                                       # number of training points
+    x = reshape(2.0 * π * rand(n), 1, n)         # predictors/features: 1 x n
+    y = reshape(sin.(x) + 0.05 * randn(n)', 1, n) # predictands/targets: 1 x n
+
+    iopairs = PairedDataContainer(x,y,data_are_columns=true)
+    # Construct kernel:
+    # Squared exponential kernel (note that hyperparameters are on log scale)
+    # with observational noise
+    GPkernel = SE(log(1.0), log(1.0))
+
+    # These will be the test inputs at which predictions are made
+    new_inputs = reshape([0.0, π/2, π, 3*π/2, 2*π], 1, 5)
+    
+    # Fit Gaussian Process Regression models. 
+    # GaussianProcesses.jl (GPJL) provides two predict functions, predict_y 
+    # (which predicts the random variable y(θ)) and predict_y (which predicts 
+    # the latent random variable f(θ)). 
+    # ScikitLearn's Gaussian process regression (SKLJL) only offers one 
+    # predict function, which predicts y.
+
+    ## GaussianProcess 1: GPJL, predict_y unnormalized
+    gppackage = GPJL()
+    pred_type = YType()
+    
+    gp1 = GaussianProcess(
+        gppackage;
+        kernel=GPkernel,
+        noise_learn=true,
+        prediction_type=pred_type) 
+
+    @test gp1.kernel == GPkernel
+    @test gp1.noise_learn == true
+    @test gp1.prediction_type == pred_type
+    
+    em1 = Emulator(
+        gp1,
+        iopairs,
+        obs_noise_cov=nothing,
+        normalize_inputs=false,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+
+    Emulators.optimize_hyperparameters!(em1)
+
+    μ1, σ1² = Emulators.predict(em1, new_inputs)
+    
+    @test vec(μ1) ≈ [0.0, 1.0, 0.0, -1.0, 0.0] atol=0.3
+    @test size(μ1)==(1,5)
+    @test vec(σ1²) ≈ [0.017, 0.003, 0.004, 0.004, 0.009] atol=1e-2
+
+    # GaussianProcess 2: GPJL, predict_f
+    pred_type = FType()
+
+    gp2 = GaussianProcess(
+        gppackage;
+        kernel=GPkernel,
+        noise_learn=true,
+        prediction_type=pred_type) 
+
+    em2 = Emulator(
+        gp2,
+        iopairs,
+        obs_noise_cov=nothing,
+        normalize_inputs=false,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+
+    Emulators.optimize_hyperparameters!(em2)
+
+    μ2, σ2² = Emulators.predict(em2, new_inputs)
+    # predict_y and predict_f should give the same mean
+    @test μ2 ≈ μ1 atol=1e-6
+
+
+    # GaussianProcess 3: SKLJL 
+
+    gppackage = SKLJL()
+    pred_type = YType()
+    var = ConstantKernel(constant_value=1.0)
+    se = RBF(1.0)
+    GPkernel = var * se
+
+
+    gp3 = GaussianProcess(
+        gppackage;
+        kernel=GPkernel,
+        noise_learn=true,
+        prediction_type=pred_type) 
+
+    em3 = Emulator(
+        gp3,
+        iopairs,
+        obs_noise_cov=nothing,
+        normalize_inputs=false,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+
+    Emulators.optimize_hyperparameters!(em3)
+
+    
+    #gp3 = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=nothing,
+    #               normalized=false, noise_learn=true, 
+    #	   truncate_svd=1.0, standardize=false,
+    #               prediction_type=pred_type, norm_factor=nothing)
+   
+    μ3, σ3² = Emulators.predict(em3, new_inputs)
+    @test vec(μ3) ≈ [0.0, 1.0, 0.0, -1.0, 0.0] atol=0.3
+    @test vec(σ3²) ≈ [0.016, 0.002, 0.003, 0.004, 0.003] atol=1e-2
+  
+
+    # -------------------------------------------------------------------------
+    # Test case 2: 2D input, 2D output
+    # -------------------------------------------------------------------------
+
+    gppackage = GPJL()
+    pred_type = YType()
+
+    # Generate training data
+    m = 80                                        # number of training points
+    
+    p = 2   # input dim 
+    d = 2   # output dim
+    X = 2.0 * π * rand(p, m)
+    
+    # G(x1, x2)
+    g1x = sin.(X[1, :]) .+ cos.(X[2, :])
+    g2x = sin.(X[1, :]) .- cos.(X[2, :])
+    gx = zeros(2,m)
+    gx[1,:] = g1x
+    gx[2,:] = g2x
+
+    # Add noise η
+    μ = zeros(d) 
+    Σ = 0.1 * [[0.8, 0.2] [0.2, 0.5]] # d x d
+    noise_samples = rand(MvNormal(μ, Σ), m)
+    
+    # y = G(x) + η
+    Y = gx .+ noise_samples
+
+    iopairs2 = PairedDataContainer(X,Y,data_are_columns=true)
+    @test get_inputs(iopairs2) == X
+    @test get_outputs(iopairs2) == Y
+
+    gp4 = GaussianProcess(
+        gppackage;
+        kernel=nothing,
+        noise_learn=true,
+        prediction_type=pred_type)
+
+    em4 = Emulator(
+        gp4,
+        iopairs2,
+        obs_noise_cov=Σ,
+        normalize_inputs=true,
+        standardize_outputs=false,
+        truncate_svd=1.0)
+
+    Emulators.optimize_hyperparameters!(em4)
+    
+    new_inputs = zeros(2, 4)
+    new_inputs[:, 2] = [π/2, π]
+    new_inputs[:, 3] = [π, π/2]
+    new_inputs[:, 4] = [3*π/2, 2*π]
+    
+    μ4, σ4² = Emulators.predict(em4, new_inputs, transform_to_real=true)
+
+    @test μ4[:, 1] ≈ [1.0, -1.0] atol=0.25
+    @test μ4[:, 2] ≈ [0.0, 2.0] atol=0.25
+    @test μ4[:, 3] ≈ [0.0, 0.0] atol=0.25
+    @test μ4[:, 4] ≈ [0.0, -2.0] atol=0.25
+    @test length(σ4²) == size(new_inputs,2)
+    @test size(σ4²[1]) == (d, d)    
+
+end
diff --git a/test/GaussianProcessEmulator/runtests.jl b/test/GaussianProcessEmulator/runtests.jl
deleted file mode 100644
index 9b2bf4390..000000000
--- a/test/GaussianProcessEmulator/runtests.jl
+++ /dev/null
@@ -1,190 +0,0 @@
-# Import modules
-using Random
-using Test
-using GaussianProcesses
-using Statistics 
-using Distributions
-using ScikitLearn
-using LinearAlgebra
-@sk_import gaussian_process : GaussianProcessRegressor
-@sk_import gaussian_process.kernels : (RBF, WhiteKernel, ConstantKernel)
-
-using CalibrateEmulateSample.GaussianProcessEmulator
-using CalibrateEmulateSample.DataStorage
-
-@testset "GaussianProcessEmulator" begin
-
-    # Seed for pseudo-random number generator
-    rng_seed = 41
-    Random.seed!(rng_seed)
-
-    # -------------------------------------------------------------------------
-    # Test case 1: 1D input, 1D output
-    # -------------------------------------------------------------------------
-
-    # Training data
-    n = 20                                       # number of training points
-    x = reshape(2.0 * π * rand(n), 1, n)         # predictors/features: 1 x n
-    y = reshape(sin.(x) + 0.05 * randn(n)', 1, n) # predictands/targets: 1 x n
-
-    iopairs = PairedDataContainer(x,y,data_are_columns=true)
-    # Construct kernel:
-    # Squared exponential kernel (note that hyperparameters are on log scale)
-    # with observational noise
-    GPkernel = SE(log(1.0), log(1.0))
-
-    # These will be the test inputs at which predictions are made
-    new_inputs = reshape([0.0, π/2, π, 3*π/2, 2*π], 1, 5)
-    
-    # Fit Gaussian Process Regression models. 
-    # GaussianProcesses.jl (GPJL) provides two predict functions, predict_y 
-    # (which predicts the random variable y(θ)) and predict_y (which predicts 
-    # the latent random variable f(θ)). 
-    # ScikitLearn's Gaussian process regression (SKLJL) only offers one 
-    # predict function, which predicts y.
-
-    # GaussianProcess 1: GPJL, predict_y
-    gppackage = GPJL()
-    pred_type = YType()
-
-   
-    gp1 = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=nothing, 
-                   normalized=false, noise_learn=true, 
-		   truncate_svd=1.0, standardize=false,
-                   prediction_type=pred_type, norm_factor=nothing)
-    μ1, σ1² = GaussianProcessEmulator.predict(gp1, new_inputs)
-    
-    @test gp1.input_output_pairs == iopairs
-    @test gp1.input_mean[1] ≈ mean(x) atol=1e-2
-    @test gp1.sqrt_inv_input_cov == nothing
-    @test gp1.prediction_type == pred_type
-    @test vec(μ1) ≈ [0.0, 1.0, 0.0, -1.0, 0.0] atol=0.3
-    @test size(μ1)==(1,5)
-    @test vec(σ1²) ≈ [0.017, 0.003, 0.004, 0.004, 0.009] atol=1e-2
-
-    # Check if normalization works
-    gp1_norm = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, 
-                        obs_noise_cov=nothing, normalized=true, 
-                        noise_learn=true, truncate_svd=1.0,
-			standardize=false, prediction_type=pred_type,
-			norm_factor=nothing)
-    @test gp1_norm.sqrt_inv_input_cov ≈ [sqrt(1.0 / Statistics.var(x))] atol=1e-4
-
-
-        
-    
-    # GaussianProcess 2: GPJL, predict_f
-    pred_type = FType()
-    gp2 = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=nothing, 
-                   normalized=false, noise_learn=true, 
-		   truncate_svd=1.0, standardize=false,
-                   prediction_type=pred_type, norm_factor=nothing)
-    μ2, σ2² = GaussianProcessEmulator.predict(gp2, new_inputs)
-    # predict_y and predict_f should give the same mean
-    @test μ2 ≈ μ1 atol=1e-6
-
-
-    # GaussianProcess 3: SKLJL 
-
-    gppackage = SKLJL()
-    pred_type = YType()
-    var = ConstantKernel(constant_value=1.0)
-    se = RBF(1.0)
-    GPkernel = var * se
-
-    gp3 = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=nothing, 
-                   normalized=false, noise_learn=true, 
-		   truncate_svd=1.0, standardize=false,
-                   prediction_type=pred_type, norm_factor=nothing)
-   
-    μ3, σ3² = GaussianProcessEmulator.predict(gp3, new_inputs)
-    @test vec(μ3) ≈ [0.0, 1.0, 0.0, -1.0, 0.0] atol=0.3
-    @test vec(σ3²) ≈ [0.016, 0.002, 0.003, 0.004, 0.003] atol=1e-2
-  
-
-    # -------------------------------------------------------------------------
-    # Test case 2: 2D input, 2D output
-    # -------------------------------------------------------------------------
-
-    gppackage = GPJL()
-    pred_type = YType()
-
-    # Generate training data
-    m = 80                                        # number of training points
-    
-    p = 2   # input dim 
-    d = 2   # output dim
-    X = 2.0 * π * rand(p, m)
-    
-    # G(x1, x2)
-    g1x = sin.(X[1, :]) .+ cos.(X[2, :])
-    g2x = sin.(X[1, :]) .- cos.(X[2, :])
-    gx = zeros(2,m)
-    gx[1,:] = g1x
-    gx[2,:] = g2x
-
-    # Add noise η
-    μ = zeros(d) 
-    Σ = 0.1 * [[0.8, 0.2] [0.2, 0.5]] # d x d
-    noise_samples = rand(MvNormal(μ, Σ), m)
-    
-    # y = G(x) + η
-    Y = gx .+ noise_samples
-
-    iopairs2 = PairedDataContainer(X,Y,data_are_columns=true)
-    @test get_inputs(iopairs2) == X
-    @test get_outputs(iopairs2) == Y
-
-    transformed_Y, decomposition = svd_transform(Y, Σ, truncate_svd=1.0)
-    @test size(transformed_Y) == size(Y)
-    transformed_Y, decomposition = svd_transform(Y[:, 1], Σ, truncate_svd=1.0)
-    @test size(transformed_Y) == size(Y[:, 1])
-
-    
-    gp4 = GaussianProcess(iopairs2, gppackage, GPkernel=nothing, obs_noise_cov=Σ, 
-                          normalized=true, noise_learn=true, 
-		          truncate_svd=1.0, standardize=false,
-                          prediction_type=pred_type, norm_factor=nothing)
-
-    new_inputs = zeros(2, 4)
-    new_inputs[:, 2] = [π/2, π]
-    new_inputs[:, 3] = [π, π/2]
-    new_inputs[:, 4] = [3*π/2, 2*π]
-    
-    μ4, σ4² = GaussianProcessEmulator.predict(gp4, new_inputs, transform_to_real=true)
-
-    @test μ4[:, 1] ≈ [1.0, -1.0] atol=0.25
-    @test μ4[:, 2] ≈ [0.0, 2.0] atol=0.25
-    @test μ4[:, 3] ≈ [0.0, 0.0] atol=0.25
-    @test μ4[:, 4] ≈ [0.0, -2.0] atol=0.25
-    @test length(σ4²) == size(new_inputs,2)
-    @test size(σ4²[1]) == (d, d)
-
-    # Check if standardization works
-    norm_factor = 10.0
-    norm_factor = fill(norm_factor, size(Y[:,1])) # must be size of output dim
-    println(norm_factor)
-    gp4_standardized = GaussianProcess(iopairs2, gppackage, GPkernel=nothing, obs_noise_cov=Σ, 
-                          normalized=true, noise_learn=true, 
-		          truncate_svd=1.0, standardize=true,
-                          prediction_type=pred_type, norm_factor=norm_factor)
-    cov_est = gp4_standardized.decomposition.V * diagm(gp4_standardized.decomposition.S) *
-            gp4_standardized.decomposition.Vt
-    @test cov_est ≈ Σ ./ (norm_factor .* norm_factor') atol=1e-2
-    @test cov_est ≈ Σ ./ 100.0 atol=1e-2
-
-    # Check if truncation works
-    norm_factor = 10.0
-    norm_factor = fill(norm_factor, size(Y[:,1])) # must be size of output dim
-    println(norm_factor)
-    gp4_trunc = GaussianProcess(iopairs2, gppackage, GPkernel=nothing, obs_noise_cov=Σ, 
-                          normalized=true, noise_learn=true, 
-		          truncate_svd=0.1, standardize=true,
-                          prediction_type=pred_type, norm_factor=norm_factor)
-    μ4_trunc, σ4_trunc = GaussianProcessEmulator.predict(gp4_trunc, new_inputs, transform_to_real=false)
-    μ4_trunc_real, σ4_trunc_real = GaussianProcessEmulator.predict(gp4_trunc, new_inputs, transform_to_real=true)
-    @test size(μ4_trunc,1) == 1
-    @test size(μ4_trunc_real,1) == 2
-    
-
-end
diff --git a/test/MarkovChainMonteCarlo/runtests.jl b/test/MarkovChainMonteCarlo/runtests.jl
index dbd0e8b67..cfbfd1700 100644
--- a/test/MarkovChainMonteCarlo/runtests.jl
+++ b/test/MarkovChainMonteCarlo/runtests.jl
@@ -6,7 +6,7 @@ using Test
 
 using CalibrateEmulateSample.MarkovChainMonteCarlo
 using CalibrateEmulateSample.ParameterDistributionStorage
-using CalibrateEmulateSample.GaussianProcessEmulator
+using CalibrateEmulateSample.Emulators
 using CalibrateEmulateSample.DataStorage
 
 @testset "MarkovChainMonteCarlo" begin
@@ -17,7 +17,7 @@ using CalibrateEmulateSample.DataStorage
 
     # We need a GaussianProcess to run MarkovChainMonteCarlo, so let's reconstruct the one that's tested
     # in test/GaussianProcesses/runtests.jl
-    n = 20                                       # number of training points
+    n = 40                                       # number of training points
     x = reshape(2.0 * π * rand(n), 1, n)         # predictors/features: 1 × n
     σ2_y = reshape([0.05],1,1)
     y = sin.(x) + rand(Normal(0, σ2_y[1]), (1, n)) # predictands/targets: 1 × n
@@ -34,10 +34,21 @@ using CalibrateEmulateSample.DataStorage
     # Squared exponential kernel (note that hyperparameters are on log scale)
     # with observational noise
     GPkernel = SE(log(1.0), log(1.0))
+    gp = GaussianProcess(
+        gppackage;
+        kernel=GPkernel,
+        noise_learn=true,
+        prediction_type=pred_type) 
+    
+    em = Emulator(
+        gp,
+        iopairs,
+        obs_noise_cov=σ2_y,
+        normalize_inputs=false,
+        standardize_outputs=false,
+        truncate_svd=1.0)
 
-    gp = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=σ2_y, 
-                  normalized=false, noise_learn=true, 
-                  prediction_type=pred_type) 
+    Emulators.optimize_hyperparameters!(em)
 
     ### Define prior
     umin = -1.0
@@ -62,7 +73,7 @@ using CalibrateEmulateSample.DataStorage
     mcmc_test = MCMC(obs_sample, σ2_y, prior, step, param_init, max_iter, 
                         mcmc_alg, burnin; svdflag=true, standardize=false,
 			truncate_svd=1.0, norm_factor=norm_factors)
-    new_step = find_mcmc_step!(mcmc_test, gp)
+    new_step = find_mcmc_step!(mcmc_test, em)
 
     # reset parameters 
     burnin = 1000
@@ -72,7 +83,7 @@ using CalibrateEmulateSample.DataStorage
     mcmc = MCMC(obs_sample, σ2_y, prior, step, param_init, max_iter, 
                    mcmc_alg, burnin; svdflag=true, standardize=false,
                    truncate_svd=1.0, norm_factor=norm_factors)
-    sample_posterior!(mcmc, gp, max_iter)
+    sample_posterior!(mcmc, em, max_iter)
     posterior_distribution = get_posterior(mcmc)      
     #post_mean = mean(posterior, dims=1)[1]
     posterior_mean = get_mean(posterior_distribution)
@@ -89,24 +100,4 @@ using CalibrateEmulateSample.DataStorage
                                    max_iter, "gibbs", burnin)
     @test isapprox(posterior_mean[1] - π/2, 0.0; atol=4e-1)
 
-    # Standardization and truncation
-    norm_factor = 10.0
-    norm_factor = fill(norm_factor, size(y[:,1])) # must be size of output dim
-    gp = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=σ2_y, 
-                  normalized=false, noise_learn=true, standardize=true, truncate_svd=0.9, 
-                  prediction_type=pred_type, norm_factor=norm_factor) 
-    mcmc_test = MCMC(obs_sample, σ2_y, prior, step, param_init, max_iter, 
-                        mcmc_alg, burnin;
-			svdflag=true, standardize=true, norm_factor=norm_factor, truncate_svd=0.9)
-
-    # Now begin the actual MCMC
-    mcmc = MCMC(obs_sample, σ2_y, prior, step, param_init, max_iter, 
-                   mcmc_alg, burnin; svdflag=true, standardize=false,
-                   truncate_svd=1.0, norm_factor=norm_factor)
-    sample_posterior!(mcmc, gp, max_iter)
-    posterior_distribution = get_posterior(mcmc)      
-    #post_mean = mean(posterior, dims=1)[1]
-    posterior_mean2 = get_mean(posterior_distribution)
-    @test posterior_mean2 ≈ posterior_mean atol=0.1
-
 end
diff --git a/test/runtests.jl b/test/runtests.jl
index b1cd0e225..17dae84ab 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -24,7 +24,8 @@ end
         end
     end
 
-    for submodule in ["GaussianProcessEmulator",
+    for submodule in ["Emulator",
+                      "GaussianProcess",
 		      "MarkovChainMonteCarlo",
 		      "Utilities"]