Title:	Temporal Empirical Dynamic Modeling
Version:	1.0
Description:	Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://stscl.github.io/tEDM/, https://github.com/stscl/tEDM
BugReports:	https://github.com/stscl/tEDM/issues
Depends:	R (≥ 4.1.0)
LinkingTo:	Rcpp, RcppThread, RcppArmadillo
Imports:	dplyr, ggplot2, methods, Rcpp
Suggests:	RcppThread, RcppArmadillo, readr, plot3D, spEDM, knitr, rmarkdown, purrr, tidyr, cowplot
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2025-07-11 07:30:54 UTC; 31809
Author:	Wenbo Lv [aut, cre, cph]
Maintainer:	Wenbo Lv <lyu.geosocial@gmail.com>
Repository:	CRAN
Date/Publication:	2025-07-15 12:00:02 UTC

convergent cross mapping

Description

convergent cross mapping

Usage

## S4 method for signature 'data.frame'
ccm(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  tau = 0,
  k = E + 1,
  theta = 1,
  algorithm = "simplex",
  lib = NULL,
  pred = NULL,
  threads = length(libsizes),
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE
)

Arguments

Value

A list

xmap

cross mapping results

varname

names of causal and effect variable

bidirectional

whether to examine bidirectional causality

References

Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S., 2012. Detecting Causality in Complex Ecosystems. Science 338, 496–500.

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
ccm(sim,"x","y",libsizes = seq(5,35,5),E = 8,k = 7,threads = 1)

cross mapping cardinality

Description

cross mapping cardinality

Usage

## S4 method for signature 'data.frame'
cmc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  tau = 0,
  k = pmin(E^2),
  lib = NULL,
  pred = NULL,
  threads = length(libsizes),
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE
)

Arguments

Value

A list

xmap

cross mapping results

cs

causal strength

varname

names of causal and effect variable

bidirectional

whether to examine bidirectional causality

References

Tao, P., Wang, Q., Shi, J., Hao, X., Liu, X., Min, B., Zhang, Y., Li, C., Cui, H., Chen, L., 2023. Detecting dynamical causality by intersection cardinal concavity. Fundamental Research.

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
cmc(sim,"x","y",E = 4,k = 15,threads = 1)

embedding time series data

Description

embedding time series data

Usage

## S4 method for signature 'data.frame'
embedded(data, target, E = 3, tau = 0)

Arguments

Value

A matrix

Examples

embedded(data.frame(t = 1:5),"t",3)

false nearest neighbours

Description

false nearest neighbours

Usage

## S4 method for signature 'data.frame'
fnn(
  data,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 0,
  rt = 10,
  eps = 2,
  threads = length(E)
)

Arguments

Value

A vector

References

Kennel M. B., Brown R. and Abarbanel H. D. I., Determining embedding dimension for phase-space reconstruction using a geometrical construction, Phys. Rev. A, Volume 45, 3403 (1992).

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
fnn(sim,"x",threads = 1)

intersection cardinality

Description

intersection cardinality

Usage

## S4 method for signature 'data.frame'
ic(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 0,
  k = E + 2,
  threads = length(pred),
  parallel.level = "low"
)

Arguments

Value

A list

xmap

cross mapping performance

varname

name of target variable

method

method of cross mapping

tau

step of time lag

References

Tao, P., Wang, Q., Shi, J., Hao, X., Liu, X., Min, B., Zhang, Y., Li, C., Cui, H., Chen, L., 2023. Detecting dynamical causality by intersection cardinal concavity. Fundamental Research.

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
ic(sim,"x","y",E = 4,k = 15:30,threads = 1)

logistic map

Description

logistic map

Usage

logistic_map(
  x,
  y = NULL,
  z = NULL,
  step = 15,
  alpha_x = 3.6,
  alpha_y = 3.72,
  alpha_z = 3.68,
  beta_xy = 0.05,
  beta_xz = 0.05,
  beta_yx = 0.2,
  beta_yz = 0.2,
  beta_zx = 0.35,
  beta_zy = 0.35,
  threshold = Inf,
  transient = 1
)

Arguments

Value

A data.frame

Examples

logistic_map(x = 0.2)

multispatial convergent cross mapping

Description

multispatial convergent cross mapping

Usage

## S4 method for signature 'list'
multispatialccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 0,
  k = E + 1,
  boot = 99,
  seed = 42,
  threads = length(libsizes),
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE
)

Arguments

Value

A list

xmap

cross mapping results

varname

names of causal and effect variable

bidirectional

whether to examine bidirectional causality

References

Clark, A.T., Ye, H., Isbell, F., Deyle, E.R., Cowles, J., Tilman, G.D., Sugihara, G., 2015. Spatial convergent cross mapping to detect causal relationships from short time series. Ecology 96, 1174–1181.

Examples

set.seed(42)
obs = runif(15,0,0.1)
sim = vector("list",15)
for (i in seq_along(obs)){
  sim[[i]] = logistic_map(x = obs[i],y = obs[i],step = 15,beta_xy = 0.5,beta_yx = 0)
}
lst = list(x = do.call(cbind, lapply(sim, function(df) df$x)),
           y = do.call(cbind, lapply(sim, function(df) df$y)))
multispatialccm(lst,"x","y",libsizes = 5:15,E = c(7,2),k = 6,threads = 1)

partial cross mapping

Description

partial cross mapping

Usage

## S4 method for signature 'data.frame'
pcm(
  data,
  cause,
  effect,
  conds,
  libsizes = NULL,
  E = 3,
  tau = 0,
  k = E + 1,
  theta = 1,
  algorithm = "simplex",
  lib = NULL,
  pred = NULL,
  threads = length(libsizes),
  parallel.level = "low",
  bidirectional = TRUE,
  cumulate = FALSE,
  progressbar = TRUE
)

Arguments

Value

A list

pxmap

partial cross mapping results

xmap

cross mapping results

varname

names of causal and effect variable

bidirectional

whether to examine bidirectional causality

References

Leng, S., Ma, H., Kurths, J. et al. Partial cross mapping eliminates indirect causal influences. Nat Commun 11, 2632 (2020).

Examples

sim = logistic_map(x = 0.4,y = 0.4,z = 0.4,step = 45,
                   beta_xy = 0.5, beta_xz = 0,
                   beta_yx = 0, beta_yz = 0.5,
                   beta_zx = 0, beta_zy = 0)
pcm(sim,"x","z","y",libsizes = seq(5,35,5),E = 10,k = 7,threads = 1)

simplex forecast

Description

simplex forecast

Usage

## S4 method for signature 'data.frame'
simplex(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 0,
  k = E + 1,
  threads = length(E)
)

## S4 method for signature 'list'
simplex(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 2:10,
  tau = 0,
  k = E + 1,
  threads = length(E)
)

Arguments

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

tau

step of time lag

References

Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734-741.

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
simplex(sim,"x","y",k = 7,threads = 1)

smap forecast

Description

smap forecast

Usage

## S4 method for signature 'data.frame'
smap(
  data,
  column,
  target,
  lib = NULL,
  pred = NULL,
  E = 3,
  tau = 0,
  k = E + 1,
  theta = c(0, 1e-04, 3e-04, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3,
    4, 6, 8),
  threads = length(theta)
)

Arguments

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

References

Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688):477-495.

Examples

sim = logistic_map(x = 0.4,y = 0.4,step = 45,beta_xy = 0.5,beta_yx = 0)
smap(sim,"x","y",E = 8,k = 7,threads = 1)