00880nas a2200157 4500008004100000022001400041245009800055210006900153300000700222490000600229520033900235100003400574700002300608700002300631856006800654 2017 eng d a2296-914400aA Package for Measuring Emergence, Self-organization, and Complexity Based on Shannon Entropy0 aPackage for Measuring Emergence Selforganization and Complexity a100 v43 aWe present Matlab/Octave functions to calculate measures of emergence, self-organization, and complexity of discrete and continuous data. The measures are based on Shannon's information and differential entropy, respectively. Examples from different datasets and probability distributions are used to illustrate the usage of the code.1 aSantamaría-Bonfil, Guillermo1 aGershenson, Carlos1 aFernández, Nelson uhttp://journal.frontiersin.org/article/10.3389/frobt.2017.0001001125nas a2200157 4500008004100000022001400041245005700055210005700112300000700169490000700176520066200183100003400845700002300879700002300902856004200925 2016 eng d a1099-430000aMeasuring the Complexity of Continuous Distributions0 aMeasuring the Complexity of Continuous Distributions a720 v183 aWe extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon's information, the novel continuous complexity measures describe how a system'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.1 aSantamaría-Bonfil, Guillermo1 aFernández, Nelson1 aGershenson, Carlos uhttp://www.mdpi.com/1099-4300/18/3/72