paper/cover_letter_MEE.md

In ha0ye/rEDM: Applications of Empirical Dynamic Modeling from Time Series

Dear Editors

We are pleased to submit our manuscript titled “rEDM: An R package for Empirical Dynamic Modeling and Convergent Cross Mapping” to be considered as an Applications publication in Methods in Ecology and Evolution.

Modeling ecological time series can be highly challenging. Not only are there many plausible mathematical representations for the underlying ecological processes, but time series data are often limited in length or the variables that are available, creating challenges for fitting model parameters and model selection. Empirical Dynamic Modeling (EDM) is an emerging approach that enables modeling of time series with minimal assumptions, yet with the capability of deriving insight into the underlying system processes.

Our manuscript describes rEDM, an R package for empirical dynamic modeling. It has been thoroughly tested and contains functions for many of the common tasks in analyzing time series: developing forecast models, identifying relationships between time series, and more. As of time of submission, the RStudio CRAN mirror shows over 11,000 downloads, indicative of its broad applicability and utility, for both MEE readers and beyond.

An early version of our manuscript has been included as a vignette with the R package, and we have uploaded it as a PDF. It is viewable online at https://ha0ye.github.io/rEDM/articles/rEDM.html .

We would also be pleased to have a joint review of the package by rOpenSci, though we note that because rEDM is an analysis package, it falls outside the scope of packages considered by rOpenSci. Nevertheless, our suggested reviewers have experience developing R packages and/or time series analysis.

Thank you for considering our work, and we look forward to hearing back from you.

Sincerely, Hao Ye (on behalf of the authors)

ha0ye/rEDM documentation built on March 30, 2021, 11:21 p.m.