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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rEDM_package.R
\docType{package}
\name{rEDM}
\alias{rEDM}
\alias{rEDM-package}
\title{Applications of empirical dynamic modeling from time series.}
\description{
The \pkg{rEDM} package is a new implementation of EDM algorithms
based on research software previously developed for internal use in the
Sugihara Lab (UCSD/SIO). Contains C++ compiled objects that use time delay
embedding to perform state-space reconstruction and nonlinear forecasting
and an R interface to those objects using \pkg{Rcpp}. It supports both the
simplex projection method from Sugihara & May (1990)
<DOI:10.1038/344734a0> and the S-map algorithm in Sugihara (1994)
<DOI:10.1098/rsta.1994.0106>. In addition, this package implements
convergent cross mapping as described in Sugihara et al. (2012)
<DOI:10.1126/science.1227079> and multiview embedding as described in Ye &
Sugihara (2016) <DOI:10.1126/science.aag0863>.
}
\details{
This package is divided into a set of main functions to perform
various analyses, as well as helper functions that perform minor tasks,
such as generate data, processing output, and wrapper functions.
\strong{Main Functions}:
\itemize{
\item \code{\link{simplex}} - simplex projection for univariate forecasting
\item \code{\link{s_map}} - S-maps for univariate forecasting
\item \code{\link{block_lnlp}} - simplex or S-map forecasting with a generic reconstructed state-space
\item \code{\link{ccm}} - convergent cross mapping (causal inference)
\item \code{\link{multiview}} - multi-model approach to forecasting
\item \code{\link{tde_gp}} - Gaussian Processes for univariate forecasting
\item \code{\link{block_gp}} - Gaussian Processes with a generic reconstructed state-space
}
\strong{Helper Functions}:
\itemize{
\item \code{\link{compute_stats}} - compute forecast skill metrics
\item \code{\link{ccm_means}} - aggregate output of \code{\link{ccm}} by library size (`lib_size`)
\item \code{\link{make_surrogate_data}} - generate surrogate time series
\item \code{\link{test_nonlinearity}} - test for nonlinearity using surrogate time series
}
}
\author{
\strong{Maintainer}: Hao Ye
\strong{Authors}: Adam Clark, Ethan Deyle, Steve Munch
\strong{Contributors}: Oliver Keyes, Jun Cai, Ethan White, Jane Cowles,
James Stagge, Yair Daon, Andrew Edwards, George Sugihara
}
\keyword{package}