The rEDM software package written by Joseph Park and Cameron Smith is a toolbox of
commonly used methods in empirical dynamics. It is based on laboratory research code
developed in the Sugihara Lab in the early 90's and updated by Hao Ye in the 2010's and
contains methods to identify and quantify nonlinear predictability (Sugihara & May 1990,
Sugihara 1994) as well as multivariate extensions thereof (Dixon et al. 1999, Ye and Sugihara
2016). In addition, rEDM implements convergent cross mapping as a test for causality (Sugihara
et al. 2012), and a method for measuring changing interactions among causal variables (Deyle et
A Python implementation of EDM tools written by Joseph Park and Cameron Smith providing
functionality similar to the R implementation rEDM. Functionality includes simplex projection
(Sugihara and May 1990), sequential locally weighted global linear maps (S-map) (Sugihara
1994), multivariate embeddings (Dixon et. al. 1999), convergent cross mapping (Sugihara et. al.
2012), and multiview embedding (Ye and Sugihara 2016). Options are given for regularization
schemes useful for tracking evolving interactions and for computing S-map jacobians (Cenci et
al. 2019, 2020).
The pyEDM package can be accessed here at github.
A Python implementation of EDM tools providing functionality similar to the R
implementation rEDM . Functionality includes simplex projection (Sugihara and May 1990),
sequential locally weighted global linear maps (S-map) (Sugihara 1994), multivariate
embeddings (Dixon et. al. 1999), convergent cross mapping (Sugihara et. al. 2012), and
multiview embedding (Ye and Sugihara 2016).
An advanced and highly efficient Cpp implementation of EDM machinery designed and
developed by Joseph Park and Cameron Smith to provide the computational backbone for the
current versions of rEDM and pyEDM. CppEDM was reviewed and blessed by RunTime
Analytics. Note that the early beta versions of rEDM that do not use the optimized Cpp core
(V0.7.3 and earlier) employ a syntax and logic used by Hao Ye that more closely follows the
original somewhat opaque 1990’s Sugihara code base written in C.
The Cpp project can be accessed here at GitHub.
A Stata module developed by Jinjing Li and Michael Zyphur to implement empirical dynamic
modeling,” Statistical Software Components S458593, Boston College Department of
Economics. https://econpapers.repec.org › software › bocbocode. J. Li, M. Zyphur and
G. Sugihara (2019).
A MatLab visualization tool developed by Hiroaki Natsukawa (natsukawa.hiroaki.3u@kyoto-
u.ac.jp) to track and study EDM system evolution.
Natsukawa, H., E. Deyle, G. Pao, K. Koyamada & G. Sugihara (2020) A Visual Analytics
Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling. EEE Transactions
on Visualization and Computer Graphics. Print ISSN: 1077-2626. Online ISSN: 1077-2626. DOI