diff --git a/pluto-demo.jl b/pluto-demo.jl
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--- /dev/null
+++ b/pluto-demo.jl
@@ -0,0 +1,1773 @@
+### A Pluto.jl notebook ###
+# v0.19.27
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 5bfe5ff9-0238-49a4-8e51-cb16e3722d38
+using SoleLogics
+
+# ╔═╡ cdcdc74f-3267-4cbf-9f8d-08559ddb39e7
+begin
+	using Graphs
+
+	# Instantiate a frame with 5 worlds and 5 edges
+	worlds = SoleLogics.World.(1:5)
+	edges = Edge.([ (1, 2), (1, 3), (2, 4), (3, 4), (3, 5)])
+	fr = SoleLogics.ExplicitCrispUniModalFrame(worlds, Graphs.SimpleDiGraph(edges))
+end
+
+# ╔═╡ 65141ec2-4da9-11ee-2a0a-8974a3ec37da
+md"""
+# Sole.jl Demo
+"""
+
+# ╔═╡ 4b6b5e78-3754-4d67-9243-7722e0203aeb
+md"""
+### Propositional Logic
+"""
+
+# ╔═╡ 065f6660-f44f-483e-8f39-cb1bd116ce1f
+Proposition("p")
+
+# ╔═╡ 0deca737-1bbe-49fa-927c-90d16e4cf3f6
+Proposition{String}("p")
+
+# ╔═╡ 76b644a6-a2e5-4794-bd2d-aeaf0cb7f61a
+Proposition(123)
+
+# ╔═╡ 2dd37d36-b86b-4e9c-afd6-ce1084669a11
+Proposition{Int64}(123)
+
+# ╔═╡ c91e0387-276b-4869-9a5e-f45e79479d38
+# Instantiate a propositional interpretation
+I = TruthDict(["p" => true, "q" => false])
+
+# ╔═╡ eba17ad9-9ceb-481a-9067-fee9953defaf
+# Parse a formula from string
+φ2 = parseformula("¬(p ∧ q)")
+
+# ╔═╡ 198be476-f170-482a-9d67-55d174ecc2da
+# Parse a formula with integer as propositions
+φ3 = parseformula("¬(1 ∧ 2)"; proposition_parser = (x -> Proposition(parse(Int64, x))))
+
+# ╔═╡ 32cc4dca-8bf0-42d2-b638-b3ac793407fe
+md"""
+### Modal Logic
+"""
+
+# ╔═╡ 7c533604-a185-4b52-837c-420f2078840f
+# Pick the first world
+w1 = first(worlds)
+
+# ╔═╡ 086b2895-c2e7-43fd-9421-9c85cce7b0e9
+# Enumerate its accessible worlds
+accessibles(fr, w1)
+
+# ╔═╡ c19915eb-dd93-4b2d-9be3-a3b3ce621005
+md"""
+### Logisets
+"""
+
+# ╔═╡ ecd7377b-1a81-42e4-b216-80cf4184e0ea
+begin
+	# Build set of features
+	features = [UnivariateMin{Float64}(i_variable) for i_variable in 1:ncol(X_df)]
+	syntaxstring.(features)
+end
+
+# ╔═╡ 0256bd26-558e-4842-93a3-e882bdfe10be
+# Construct a logiset from a DataFrame
+logiset = scalarlogiset(X_df, features)
+
+# ╔═╡ ae1b7375-e85f-48ff-abe4-b1b476d217f0
+# Check the formula on the first interval of every instance
+[check(φ, logiset, i, SoleModels.Interval(1,2)) for i in 1:ninstances(X_df)]
+
+# ╔═╡ dea67564-e905-4a49-9717-ca8071a4d489
+md"""
+## Learning and inspecting a Modal Decision Tree
+"""
+
+# ╔═╡ 99b1da9b-517a-4a84-8d0c-26e9f9ff6e15
+# Instantiate the modal extension of CART, that uses relations from the coarser Interval Algebra "IA7"
+model = ModalDecisionTree(; relations = :IA7)
+
+# ╔═╡ 6b494cfc-485d-4f56-9221-9923fc44a4e5
+# Check the formula on the interpretation
+check(φ, I)
+
+# ╔═╡ 9fe948e1-699d-491e-990e-0483ed187b9d
+# Produce a string representations of the formula
+begin
+	println("Plain:\t\t\t\t" * syntaxstring(φ))
+	println("Function notation:\t" * syntaxstring(φ; function_notation = true))
+	println("With paretheses:\t" * syntaxstring(φ; remove_redundant_parentheses = false))
+end
+
+# ╔═╡ 9a9d0d2f-df64-4cca-aa22-0b75787f18e3
+begin
+	# Construct the global formula: "there exists at least one interval that..."
+	glob_φ = global_diamond(φ)
+	syntaxstring(glob_φ; variable_names_map = names(X_df))
+end
+
+# ╔═╡ 61e70caa-3365-4efb-9b83-509fb4470ebe
+# Check the global formula (note how the world does not matter, since its a global formula)
+[check(glob_φ, logiset, i) for i in 1:ninstances(logiset)]
+
+# ╔═╡ a14b58da-73e5-4c16-9136-343e289be763
+begin
+	# Create a new logical operator `⊕`
+	import SoleLogics: arity
+	const ⊕ = SoleLogics.NamedOperator{:⊕}()
+	SoleLogics.arity(::Type{SoleLogics.NamedOperator{:⊕}}) = 2
+
+	parseformula("¬p ∧ q") ⊕ p |> syntaxstring
+end
+
+# ╔═╡ 2f15f508-9bb3-452f-a831-f60144457fa4
+begin
+	# Give XOR semantics to the operator `⊕`
+	import SoleLogics: collatetruth
+	SoleLogics.collatetruth(::SoleLogics.BooleanAlgebra, ::typeof(⊕), (t1, t2)::NTuple{2,Bool}) = Base.xor(t1, t2)
+	check(φ, I)
+end
+
+# ╔═╡ d3fb3373-3fae-4aca-90b7-718535252703
+# Generate a random formula with the new operator `⊕`
+φ5 = randformula(Random.MersenneTwister(5), 2, [p,q], [¬, ∨, ⊕])
+
+# ╔═╡ a01c8457-42e6-47ed-bfca-bd8c278d9574
+# Generate a random MODAL formula
+φ10 = randformula(Random.MersenneTwister(14), 3, [p,q], SoleLogics.BASE_MODAL_OPERATORS)
+
+# ╔═╡ bf9f189d-f11d-4830-888d-b5953d6b5690
+begin
+	# Assign each world a propositional interpretation
+	valuation = Dict([
+	        worlds[1] => TruthDict([p => true, q => false]),
+	        worlds[2] => TruthDict([p => true, q => true]),
+	        worlds[3] => TruthDict([p => true, q => false]),
+	        worlds[4] => TruthDict([p => false, q => false]),
+	        worlds[5] => TruthDict([p => false, q => true]),
+	     ])
+
+	# Instantiate a Kripke structure!
+	K = KripkeStructure(fr, valuation)
+
+	[w => check(φ, K, w) for w in worlds]
+end
+
+# ╔═╡ 7655458a-d5af-44d9-9ff6-74db67f3d5d4
+# Print model
+report(mach).printmodel()
+
+# ╔═╡ 66ec0d97-a221-4363-a21d-1c15835645d2
+begin
+	# Print model in a condensed form (useful for checking which variables were useful)
+	report(mach).printmodel(false)
+
+	# Feature importances
+	feature_importances(mach)
+end
+
+# ╔═╡ b6d6ed79-9abc-47a6-bbbc-1a1610ffb830
+begin
+	# Access model
+	tree_train = report(mach).model
+
+	# Extract the corresponding ruleset
+	ruleset = listrules(tree_train);
+
+	# Print ruleset
+	printmodel.(ruleset; show_metrics = true, threshold_digits = 2, variable_names_map = [names(X_df)], parentheses_at_propositions = false);
+end
+
+# ╔═╡ 00310a1c-c4f4-43bc-a60c-a6113434f242
+begin
+	# Sprinkle the model with the test instances!
+	predictions, tree_test = report(mach).sprinkle(X_df[test_idxs,:], y[test_idxs]);
+
+	# Extract ruleset and print its metrics 
+	ruleset_test = listrules(tree_test)
+
+	printmodel.(ruleset_test; show_metrics = true, threshold_digits = 2, variable_names_map = [names(X_df)]);
+end
+
+# ╔═╡ b3417b90-c910-407a-939b-5190602c5899
+begin
+	# In the classification scenario, rules for the same class can be joined via logical conjunction (∨)
+	joined_ruleset_test = joinrules(ruleset_test)
+	printmodel.(joined_ruleset_test; show_metrics = true, variable_names_map = [names(X_df)], threshold_digits = 3);
+end
+
+# ╔═╡ 3cdf0a35-edd0-4a02-9410-94e828d0f519
+# This model has a decent accuracy by the way. TODO
+evaluate!(mach,
+        resampling=StratifiedCV(shuffle=true),
+        measures=[accuracy],
+        verbosity=0,
+        check_measure=false
+)
+
+
+# ╔═╡ c371c4a8-6b16-4e49-ba94-2dfb433618d2
+begin
+	# Build a formula on scalar conditions
+	condition1 = ScalarCondition(features[1], >, -0.5)
+	condition2 = ScalarCondition(features[2], <, 0)
+	φ = Proposition(condition1) ∧ Proposition(condition2)
+	syntaxstring(φ; variable_names_map = names(X_df))
+end
+
+# ╔═╡ 7685d19e-cc98-4031-a6f9-29ecccc9f417
+begin
+	using SoleModels
+	using DataFrames
+	
+	# Load an example time-series classification dataset as a tuple (DataFrame, Vector{String})
+	X_df, y = SoleModels.load_arff_dataset("NATOPS")
+end
+
+# ╔═╡ 655e01fd-f369-4a45-be51-f4d35dfa08c3
+# Create propositions
+p, q = Proposition.(["p", "q"])
+
+# ╔═╡ 1ea1d21e-d2c8-40f1-8b81-fe99504c2eed
+begin
+	using Random
+	
+	# Create a random formula
+	φ4 = randformula(Random.MersenneTwister(107), 3, [p,q], SoleLogics.BASE_PROPOSITIONAL_OPERATORS)
+	
+	φ4 |> syntaxstring |> println
+	
+	# Minimize the formula (according to pre-defined, simple rules, e.g., De Morgan)
+	normalize(φ4) |> syntaxstring |> println
+end
+
+# ╔═╡ 9ee33338-6985-4745-ab74-7caeac73496e
+begin
+	using MLJ
+	using Random
+	using DataFrames
+	
+	using ModalDecisionTrees
+	using SoleModels
+	
+	# Load an example time-series classification dataset as a tuple (DataFrame, Vector{String})
+	X_df, y = SoleModels.load_arff_dataset("NATOPS")
+end
+
+# ╔═╡ a00c4abe-5534-4c42-9464-d5a6867b9802
+begin
+	# Randomly split the data: 20% training, 80% testing
+	N = nrow(X_df)
+	p = randperm(Random.MersenneTwister(1), N)
+	train_idxs, test_idxs = p[1:round(Int, N*.2)], p[round(Int, N*.2)+1:end]
+	
+	mach = machine(model, X_df, y)
+	
+	# Train.
+	@time fit!(mach; rows=train_idxs)
+	
+	# Compute accuracy 
+	yhat = predict_mode(mach, X_df[test_idxs,:])
+	MLJ.accuracy(yhat, y[test_idxs])
+end
+
+# ╔═╡ be89d82c-8c79-4619-abf3-73a8698bd961
+# Manually compose a formula
+φ = ¬(p ∧ q)
+
+# ╔═╡ 00000000-0000-0000-0000-000000000001
+PLUTO_PROJECT_TOML_CONTENTS = """
+[deps]
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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+ModalDecisionTrees = "e54bda2e-c571-11ec-9d64-0242ac120002"
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+SoleModels = "4249d9c7-3290-4ddd-961c-e1d3ec2467f8"
+
+[compat]
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+MLJ = "~0.19.5"
+ModalDecisionTrees = "~0.1.6"
+SoleLogics = "~0.4.8"
+SoleModels = "~0.2.4"
+"""
+
+# ╔═╡ 00000000-0000-0000-0000-000000000002
+PLUTO_MANIFEST_TOML_CONTENTS = """
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+
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+    [deps.SpecialFunctions.weakdeps]
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+    [deps.StatsFuns.weakdeps]
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+    TransducersOnlineStatsBaseExt = "OnlineStatsBase"
+    TransducersReferenceablesExt = "Referenceables"
+
+    [deps.Transducers.weakdeps]
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+[[deps.ZipFile]]
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+[[deps.catch22_jll]]
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+
+[[deps.libpng_jll]]
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+[[deps.nghttp2_jll]]
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+[[deps.p7zip_jll]]
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+[[deps.pprof_jll]]
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+uuid = "cf2c5f97-e748-59fa-a03f-dda3c62118cb"
+version = "1.0.1+0"
+"""
+
+# ╔═╡ Cell order:
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