Rename package to Associations.jl (#377)

* package rename

* fix docs build

Author: kahaaga

LICENSE.md

@@ -1,4 +1,4 @@
-The CausalityTools.jl package is licensed under the MIT "Expat" License:
+The Associations.jl package (previously called CausalityTools.jl) is licensed under the MIT "Expat" License:
 
 > Copyright (c) 2018: Kristian Agasøster Haaga.
 >

Project.toml

@@ -1,8 +1,8 @@
-name = "CausalityTools"
-uuid = "5520caf5-2dd7-5c5d-bfcb-a00e56ac49f7"
+name = "Associations"
+uuid = "614afb3a-e278-4863-8805-9959372b9ec2"
 authors = ["Kristian Agasøster Haaga <[email protected]>", "Tor Einar Møller <[email protected]>", "George Datseris <[email protected]>"]
-repo = "https://github.com/kahaaga/CausalityTools.jl.git"
-version = "3.0.0"
+repo = "https://github.com/kahaaga/Associations.jl.git"
+version = "4.0.0"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

README.md

@@ -1,15 +1,15 @@
-# CausalityTools
+# Associations
 
-[![CI](https://github.com/juliadynamics/CausalityTools.jl/workflows/CI/badge.svg)](https://github.com/JuliaDynamics/CausalityTools.jl/actions)
-[![](https://img.shields.io/badge/docs-latest_tagged-blue.svg)](https://juliadynamics.github.io/CausalityTools.jl/stable/)
-[![](https://img.shields.io/badge/docs-dev_(master)-blue.svg)](https://juliadynamics.github.io/CausalityTools.jl/dev/)
-[![codecov](https://codecov.io/gh/JuliaDynamics/CausalityTools.jl/branch/master/graph/badge.svg?token=0b71n6x6AP)](https://codecov.io/gh/JuliaDynamics/CausalityTools.jl)
+[![CI](https://github.com/juliadynamics/Associations.jl/workflows/CI/badge.svg)](https://github.com/JuliaDynamics/Associations.jl/actions)
+[![](https://img.shields.io/badge/docs-latest_tagged-blue.svg)](https://juliadynamics.github.io/Associations.jl/stable/)
+[![](https://img.shields.io/badge/docs-dev_(master)-blue.svg)](https://juliadynamics.github.io/Associations.jl/dev/)
+[![codecov](https://codecov.io/gh/JuliaDynamics/Associations.jl/branch/master/graph/badge.svg?token=0b71n6x6AP)](https://codecov.io/gh/JuliaDynamics/Associations.jl)
 [![DOI](https://zenodo.org/badge/135443027.svg)](https://zenodo.org/badge/latestdoi/135443027)
 
-CausalityTools.jl is a package for quantifying associations, independence testing and causal inference.
+Associations.jl is a package for quantifying associations, independence testing and causal inference.
 
 All further information is provided in the
-[documentation](https://juliadynamics.github.io/CausalityTools.jl/dev), which you can either
+[documentation](https://juliadynamics.github.io/Associations.jl/dev), which you can either
 find online or build locally by running the `docs/make.jl` file.
 
 ## Key features
@@ -31,4 +31,6 @@ we also offer
 
 ## Installation
 
-To install the package, run `import Pkg; Pkg.add("CausalityTools")`.
+To install the package, run `import Pkg; Pkg.add("Associations")`.
+
+*Previously, this package was called CausalityTools.jl*.

docs/Project.toml

@@ -1,6 +1,5 @@
 [deps]
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
-CausalityTools = "5520caf5-2dd7-5c5d-bfcb-a00e56ac49f7"
 ComplexityMeasures = "ab4b797d-85ee-42ba-b621-05d793b346a2"
 DelayEmbeddings = "5732040d-69e3-5649-938a-b6b4f237613f"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"

docs/make.jl

@@ -6,12 +6,12 @@ using DocumenterInterLinks
 import Downloads
 
 # Packages used in the doc build.
-using CausalityTools
+using Associations
 using ComplexityMeasures
 using StateSpaceSets
 
 pages = [
-    "CausalityTools.jl" => "index.md",
+    "Associations.jl" => "index.md",
     "Core API reference" => [
         "Association measures" => "associations.md",
         "Independence" => "independence.md",
@@ -43,7 +43,7 @@ bibliography = CitationBibliography(
     style=:authoryear
 )
 
-build_docs_with_style(pages, CausalityTools, ComplexityMeasures, StateSpaceSets;
+build_docs_with_style(pages, Associations, ComplexityMeasures, StateSpaceSets;
     expandfirst = ["index.md"],
     bib = bibliography,
     pages = pages,

docs/src/api/counts_and_probabilities_api.md

@@ -1,17 +1,21 @@
 
+```@meta
+CollapsedDocStrings = true
+```
+
 # [Multivariate counts and probabilities API](@id counts_and_probabilities_api)
 
-For counting and probabilities, CausalityTools.jl extends the single-variable machinery
+For counting and probabilities, Associations.jl extends the single-variable machinery
 in ComplexityMeasures.jl to multiple variables.
 
 ```@docs
-CausalityTools.Counts
-CausalityTools.counts(::OutcomeSpace)
+Associations.Counts
+Associations.counts(::OutcomeSpace)
 ```
 
 ```@docs
-CausalityTools.Probabilities
-CausalityTools.probabilities(::OutcomeSpace)
+Associations.Probabilities
+Associations.probabilities(::OutcomeSpace)
 ```
 
 The utility function [`marginal`](@ref) is also useful.
@@ -26,7 +30,7 @@ Estimating multivariate counts (contingency matrices) and PMFs is simple. If the
 we can use [`UniqueElements`](@ref) to simply count the number of occurrences.
 
 ```@example counts_probs_tutorial
-using CausalityTools
+using Associations
 n = 50 # the number of samples must be the same for each input variable
 x = rand(["dog", "cat", "snake"], n)
 y = rand(1:4, n)
@@ -46,7 +50,7 @@ For numerical data, we can estimate both counts and probabilities using [`Codify
 with any count-based [`OutcomeSpace`](@ref).
 
 ```@example counts_probs_tutorial
-using CausalityTools
+using Associations
 x, y = rand(100), rand(100)
 discretization = CodifyVariables(BubbleSortSwaps(m = 4))
 probabilities(discretization, x, y)
@@ -55,7 +59,7 @@ probabilities(discretization, x, y)
 For more fine-grained control, we can use [`CodifyPoints`](@ref) with one or several [`Encoding`](@ref)s.
 
 ```@example counts_probs_tutorial
-using CausalityTools
+using Associations
 x, y = StateSpaceSet(rand(1000, 2)), StateSpaceSet(rand(1000, 3))
 
  # min/max of the `rand` call is 0 and 1

docs/src/api/cross_map_api.md

@@ -40,7 +40,7 @@ Let's reproduce figure 3A too, focusing only on [`ConvergentCrossMapping`](@ref)
 For this example, they use a bidirectional system with asymmetrical coupling strength.
 
 ```@example MAIN_CCM
-using CausalityTools
+using Associations
 using Statistics
 using LabelledArrays
 using StaticArrays
@@ -201,7 +201,7 @@ slightly different embedding.
 
 
 ```@example MAIN_CCM
-using CausalityTools
+using Associations
 using Statistics
 using LabelledArrays
 using StaticArrays
@@ -267,7 +267,7 @@ variables ``X`` and ``Y``, where ``X`` drives ``Y``.
 After we have computed the PAI in both directions, we define a measure of directionality as the difference between PAI in the ``X \to Y`` direction and in the ``Y \to X`` direction, so that if ``X`` drives ``Y``, then ``\Delta < 0``.
 
 ```@example MAIN_CCM
-using CausalityTools
+using Associations
 using LabelledArrays
 using StaticArrays
 using DynamicalSystemsBase

docs/src/api/discretization_counts_probs_api.md

@@ -12,7 +12,7 @@ When discretizing, what happens is that we "encode" input data into an intermedi
 - Once a dataset has been encoded into integers, we can estimate [`Counts`](@ref) or [`Probabilities`](@ref) ([tutorial](@ref tutorial_probabilities)).
 - Once probabilities have been estimated, one can use these to estimate [`MultivariateInformationMeasure`](@ref) ([tutorial](@ref tutorial_infomeasures)).
 
- The following functions and types are used by CausalityTools.jl to perform discretization of input data.
+ The following functions and types are used by Associations.jl to perform discretization of input data.
 
 ```@docs
 Discretization
@@ -21,7 +21,7 @@ CodifyPoints
 codify
 ```
 
-In summary, the two main ways of discretizing data in CausalityTools are as follows.
+In summary, the two main ways of discretizing data in Associations are as follows.
 
 - The [`CodifyPoints`](@ref) discretization scheme encodes input data on a point-by-point 
     basis by applying some [`Encoding`](@ref) to each point.
@@ -66,7 +66,7 @@ We'll here use the [`OrdinalPatternEncoding`](@ref) with differing parameter `m`
 multiple [`StateSpaceSet`](@ref) of differing dimensions.
 
 ```@example example_encode_points
-using CausalityTools
+using Associations
 using StateSpaceSets
 using Random; rng = Xoshiro(1234)
 
@@ -128,7 +128,7 @@ Some [`OutcomeSpace`](@ref)s dictate a sliding window which has the width of one
 when used with [`CodifyVariables`](@ref). [`ValueBinning`](@ref) is such an outcome space.
 
 ```@example example_encode_vars
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 
 x = rand(rng, 100)
@@ -150,7 +150,7 @@ end of each input variable are lost. For example, with [`OrdinalPatterns`](@ref)
 of encoded points decrease with the embedding parameter `m`.
 
 ```@example example_encode_vars
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 
 x = rand(rng, 100)
@@ -163,7 +163,7 @@ the same outcome space, as long as the operation will result in the *same* numbe
 data points for each column.
 
 ```@example example_encode_vars
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 
 x = rand(rng, 100)

docs/src/api/information_multivariate_api.md

@@ -60,7 +60,7 @@ Amplitude
 ## [A small tutorial](@id tutorial_infomeasures)
 
 
-CausalityTools.jl extends the single-variate information API in
+Associations.jl extends the single-variate information API in
 [ComplexityMeasures.jl](https://github.com/JuliaDynamics/ComplexityMeasures.jl)
 to information measures of multiple variables. 
 
@@ -115,7 +115,7 @@ expressed as a function of a joint pmf, we can use the [`JointProbabilities`](@r
 estimator.
 
 ```@example INFO_TUTORIAL
-using CausalityTools
+using Associations
 using Random; rng = MersenneTwister(1234)
 x, y = rand(rng, 1000), rand(rng, 1000)
 
@@ -143,7 +143,7 @@ they are functions of a joint pmf, and can therefore also be estimated using the
 with 3 bins along each dimension to discretize the data.
 
 ```@example INFO_TUTORIAL
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 
 x, y = randn(rng, 1000), randn(rng, 1000)
@@ -161,7 +161,7 @@ entropies are functionals of a joint pmf, so we can still use the
 based discretization.
 
 ```@example INFO_TUTORIAL
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 
 x, y = randn(rng, 1000), randn(rng, 1000)
@@ -179,7 +179,7 @@ example, one can use dedicated [`MutualInformationEstimator`](@ref)s such as
 [`KraskovStögbauerGrassberger2`](@ref) or [`GaussianMI`](@ref):
 
 ```@example INFO_TUTORIAL
-using CausalityTools
+using Associations
 using StateSpaceSets
 # We'll construct two state space sets, to illustrate how we can discretize 
 # multidimensional inputs using `CodifyPoints`.
@@ -228,7 +228,7 @@ with any count-based [`OutcomeSpace`](@ref). Here, we'll estimate [`MIShannon`](
 one type of encoding for the first variable, and another type of encoding for the second variable.
 
 ```@example counts_probs_tutorial
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 x, y = rand(rng, 100), rand(rng, 100)
 
@@ -248,7 +248,7 @@ For more fine-grained control than [`CodifyVariables`](@ref) can offer, we can u
 data by discretizing each input variable in arbitrary ways.
 
 ```@example counts_probs_tutorial
-using CausalityTools
+using Associations
 using Random; rng = Xoshiro(1234)
 x, y = StateSpaceSet(rand(rng, 1000, 2)), StateSpaceSet(rand(rng, 1000, 3))