Add dependencies and initial files

.gitignore

@@ -4,3 +4,4 @@
 /Manifest.toml
 /docs/Manifest.toml
 /docs/build/
+.vscode

Project.toml

@@ -3,6 +3,32 @@ uuid = "af9eadfc-028e-4dd7-8324-1a9c134c7af2"
 authors = ["JoelTrent <79883375+JoelTrent@users.noreply.github.com> and contributors"]
 version = "1.0.0-DEV"
 
+[deps]
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
+AngleBetweenVectors = "ec570357-d46e-52ed-9726-18773498274d"
+Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
+Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
+ConcaveHull = "7d11a335-8259-52c9-8750-e30c45c66484"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+EllipseSampling = "7d220c50-6298-48cd-9710-1d1a8ef13806"
+FLoops = "cc61a311-1640-44b5-9fba-1b764f453329"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+LatinHypercubeSampling = "a5e1c1ea-c99a-51d3-a14d-a9a37257b02d"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"
+NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
+SnoopPrecompile = "66db9d55-30c0-4569-8b51-7e840670fc0c"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+TrackingHeaps = "d94bfb22-46ad-56c3-87d0-f6c298cb800e"
+TravelingSalesmanHeuristics = "8c8f4381-2cdd-507c-846c-be2bcff6f45f"
+
 [compat]
 julia = "1.6"
 

src/NLopt_optimiser.jl

@@ -0,0 +1,38 @@
+# Numerical optimisation 
+function optimise(fun, θ₀, lb, ub;
+    dv = false,
+    method = dv ? :LD_LBFGS : :LN_BOBYQA,
+    )
+
+    if dv || String(method)[2] == 'D'
+        tomax = fun
+    else
+        tomax = (θ,∂θ) -> fun(θ)
+    end
+
+    opt = Opt(method,length(θ₀))
+    opt.max_objective = tomax
+    opt.lower_bounds = lb       # Lower bound
+    opt.upper_bounds = ub       # Upper bound
+    opt.local_optimizer = Opt(:LN_NELDERMEAD, length(θ₀))
+    res = optimize(opt, θ₀)
+    return res[[2,1]]
+end
+
+function optimise_unbounded(fun, θ₀;
+    dv = false,
+    method = dv ? :LD_LBFGS : :LN_BOBYQA,
+    )
+
+    if dv || String(method)[2] == 'D'
+        tomax = fun
+    else
+        tomax = (θ,∂θ) -> fun(θ)
+    end
+
+    opt = Opt(method,length(θ₀))
+    opt.max_objective = tomax
+    opt.local_optimizer = Opt(:LN_NELDERMEAD, length(θ₀))
+    res = optimize(opt, θ₀)
+    return res[[2,1]]
+end

src/PlaceholderLikelihood.jl

@@ -1,5 +1,100 @@
 module PlaceholderLikelihood
 
-# Write your package code here.
+using NLopt, Roots
+using ForwardDiff, LinearAlgebra
+using Random, StatsBase, Combinatorics
+using DataStructures, DataFrames, Accessors, StaticArrays
+using EllipseSampling
+using LatinHypercubeSampling
+using Distributed, FLoops
+using ConcaveHull
+using Distances, TravelingSalesmanHeuristics
+using Clustering, Meshes
+using AngleBetweenVectors
+using TrackingHeaps
 
+using ProgressMeter
+const global PROGRESS__METER__DT = 1.0
+
+
+export AbstractEllipseMLEApprox, AbstractCoreLikelihoodModel, AbstractLikelihoodModel,
+    EllipseMLEApprox, CoreLikelihoodModel, LikelihoodModel, 
+
+    AbstractProfileType, AbstractEllipseProfileType, LogLikelihood, EllipseApprox, EllipseApproxAnalytical
+    AbstractSampleType, UniformGridSamples, UniformRandomSamples, LatinHypercubeSamples
+
+    AbstractConfidenceStruct, PointsAndLogLikelihood, 
+    SampledConfidenceStruct, UnivariateConfidenceStruct, BivariateConfidenceStruct,
+
+    AbstractBivariateMethod, AbstractBivariateVectorMethod, bivariate_methods,
+    IterativeBoundaryMethod, RadialMLEMethod, RadialRandomMethod, SimultaneousMethod, Fix1AxisMethod,
+    AnalyticalEllipseMethod, ContinuationMethod, 
+
+    AbstractPredictionStruct, PredictionStruct,
+
+    initialiseLikelihoodModel,
+    getMLE_ellipse_approximation!, check_ellipse_approx_exists!
+    setθmagnitudes!, setbounds!,
+
+    transformbounds, transformbounds_NLopt,
+
+    univariate_confidenceintervals!, get_points_in_interval!,
+    bivariate_confidenceprofiles!, minimum_perimeter_polygon!,
+    dimensional_likelihood_sample!, bivariate_concave_hull,
+    full_likelihood_sample!,
+
+    add_prediction_function!, check_prediction_function_exists,
+    generate_predictions_bivariate!, generate_predictions_univariate!, generate_predictions_dim_samples!
+
+include("NLopt_optimiser.jl")
+
+# TYPES ##############################################################
+include("types/bivariate_methods.jl")
+include("types/levelsets.jl")
+include("types/predictions.jl")
+include("types/profiletype.jl")
+include("types/likelihoodmodel.jl")
+
+
+include("model_initialiser.jl")
+# include("combination_relationships.jl")
+include("transform_bounds.jl")
+include("common_profile_likelihood.jl")
+include("ellipse_likelihood.jl")
+include("predictions.jl")
+include("plotting_functions.jl")
+
+
+# UNIVARIATE METHODS #################################################
+include("univariate_methods/init_and_array_mapping.jl")
+include("univariate_methods/loglikelihood_functions.jl")
+include("univariate_methods/univariate_profile_likelihood.jl")
+include("univariate_methods/points_in_interval.jl")
+
+
+# BIVARIATE METHODS ##################################################
+include("bivariate_methods/init_and_array_mapping.jl")
+include("bivariate_methods/findpointon2Dbounds.jl")
+include("bivariate_methods/loglikelihood_functions.jl")
+include("bivariate_methods/fix1axis.jl")
+include("bivariate_methods/vectorsearch.jl")
+include("bivariate_methods/continuation_polygon_manipulation.jl")
+include("bivariate_methods/continuation.jl")
+include("bivariate_methods/iterativeboundary.jl")
+include("bivariate_methods/bivariate_profile_likelihood.jl")
+include("bivariate_methods/MPP_TSP.jl")
+
+
+# SAMPLING METHODS ###################################################
+include("dimensional_methods/full_likelihood_sampling.jl")
+include("dimensional_methods/dimensional_likelihood_sampling.jl")
+include("dimensional_methods/bivariate_concave_hull.jl")
+
+
+import SnoopPrecompile
+
+SnoopPrecompile.@precompile_all_calls begin
+    1==2
 end
+
+end

src/bivariate_methods/MPP_TSP.jl

@@ -0,0 +1,11 @@
+"""
+    minimum_perimeter_polygon!(points::Array{<:Real,2})
+
+Given a set of N points that define a boundary polygon in a 2 row, N column array, solve a minimum perimeter polygon TSP problem, reorder these points in place and return the path used (vertices in order of visitation). Uses [TravelingSalesmanHeuristics.jl](https://github.com/evanfields/TravelingSalesmanHeuristics.jl).
+"""
+function minimum_perimeter_polygon!(points::Array{<:Real,2})
+    Dij = pairwise(Euclidean(), points, dims=2)
+    path, _ = solve_tsp(Dij)
+    points .= points[:,path[1:end-1]]
+    return path
+end