@article {10.3389/frobt.2017.00010,
	title = {A Package for Measuring Emergence, Self-organization, and Complexity Based on Shannon Entropy},
	journal = {Frontiers in Robotics and AI},
	volume = {4},
	year = {2017},
	pages = {10},
	abstract = {We present Matlab/Octave functions to calculate measures of emergence, self-organization, and complexity of discrete and continuous data. The measures are based on Shannon{\textquoteright}s information and differential entropy, respectively. Examples from different datasets and probability distributions are used to illustrate the usage of the code.},
	issn = {2296-9144},
	doi = {10.3389/frobt.2017.00010},
	url = {http://journal.frontiersin.org/article/10.3389/frobt.2017.00010},
	author = {Santamar{\'\i}a-Bonfil, Guillermo and Gershenson, Carlos and Fern{\'a}ndez, Nelson}
}
@article {CxContinuous2016,
	title = {Measuring the Complexity of Continuous Distributions},
	journal = {Entropy},
	volume = {18},
	number = {3},
	year = {2016},
	pages = {72},
	abstract = {We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon{\textquoteright}s information, the novel continuous complexity measures describe how a system{\textquoteright}s predictability changes in terms of the probability distribution parameters. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.},
	issn = {1099-4300},
	doi = {10.3390/e18030072},
	url = {http://www.mdpi.com/1099-4300/18/3/72},
	author = {Santamar{\'\i}a-Bonfil, Guillermo and Fern{\'a}ndez, Nelson and Gershenson, Carlos}
}
