CausalityTools.jl

CausalityTools is a Julia package that provides algorithms for detecting dynamical influences and causal inference based on time series data, and other commonly used measures of dependence and association.

Info

You are reading the development version of the documentation of CausalityTools.jl that will become version 2.0.

Content

The goal of CausalityTools.jl is to provide an easily extendable library of univariate, bivariate and multivariate measures of complexity, association and (directional) dependence between data of various kinds. We currently offer:

Other measures are found in the menu.

Input data

Input data for CausalityTools are given as:

  • Univariate timeseries, which are given as standard Julia Vectors.
  • Multivariate timeseries, datasets, or state space sets, which are given as Datasets. Many methods convert timeseries inputs to Dataset for faster internal computations.
  • Categorical data can be used with ContingencyMatrix to compute various information theoretic measures and is represented using any iterable whose elements can be any arbitrarily complex data type (as long as it's hashable), for example Vector{String}, {Vector{Int}}, or Vector{Tuple{Int, String}}.
StateSpaceSets.DatasetType
Dataset{D, T} <: AbstractDataset{D,T}

A dedicated interface for datasets. It contains equally-sized datapoints of length D, represented by SVector{D, T}. These data are a standard Julia Vector{SVector}, and can be obtained with vec(dataset).

When indexed with 1 index, a dataset is like a vector of datapoints. When indexed with 2 indices it behaves like a matrix that has each of the columns be the timeseries of each of the variables.

Dataset also supports most sensible operations like append!, push!, hcat, eachrow, among others, and when iterated over, it iterates over its contained points.

Description of indexing

In the following let i, j be integers, typeof(data) <: AbstractDataset and v1, v2 be <: AbstractVector{Int} (v1, v2 could also be ranges, and for massive performance benefits make v2 an SVector{X, Int}).

  • data[i] == data[i, :] gives the ith datapoint (returns an SVector)
  • data[v1] == data[v1, :], returns a Dataset with the points in those indices.
  • data[:, j] gives the jth variable timeseries, as Vector
  • data[v1, v2], data[:, v2] returns a Dataset with the appropriate entries (first indices being "time"/point index, while second being variables)
  • data[i, j] value of the jth variable, at the ith timepoint

Use Matrix(dataset) or Dataset(matrix) to convert. It is assumed that each column of the matrix is one variable. If you have various timeseries vectors x, y, z, ... pass them like Dataset(x, y, z, ...). You can use columns(dataset) to obtain the reverse, i.e. all columns of the dataset in a tuple.

Info

This package has been and is under heavy development. Don't hesitate to submit an issue if you find something that doesn't work or doesn't make sense, or if there's some functionality that you're missing. Pull requests are also very welcome!