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synchrony R package

Download and Install

To download the development version of the package, type the following at the R command line:

install.packages("devtools")
devtools::install_github("tgouhier/synchrony")

To download the release version of the package on CRAN, type the following at the R command line:

install.packages("synchrony")

About synchrony

The synchrony package contains methods for computing spatial, temporal, and spatiotemporal statistics such as:

  • empirical univariate, bivariate and multivariate variograms
  • fitting variogram models
  • phase locking and synchrony analysis
  • generating autocorrelated and cross-correlated matrices

The package is fully described in Gouhier and Guichard (2014).

References

Gouhier, T. C., and F. Guichard. 2014. Synchrony: quantifying variability in space and time. Methods in Ecology and Evolution 5:524–533.

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Install

install.packages('synchrony')

Monthly Downloads

223

Version

0.3.8

License

GPL (>= 2)

Issues

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Maintainer

Last Published

December 5th, 2019

Functions in synchrony (0.3.8)

correlated.matrix

correlated.matrix
peaks

Find the proportion of local minima/maxima common to both time series and compute its significance via Monte Carlo randomizations
find.minmax

Find min/max of a time series
kendall.w

Kendall's W
bird.traits

bird trait dataset
community.sync

Compute community-wide synchrony and its significance via Monte Carlo randomizations
vario.func

vario.func
vario.fit

vario.fit
phase.sync

Phase synchrony of quasi-periodic time series
pisco.data

PISCO multi-year and spatially-explicit mussel and environmental dataset
plot.variofit

Plot variofit objects
surrogate.ts

Create surrogate time series via Markov process
plot.synchrony

Plot synchrony objects
synchrony-package

Methods for Computing Spatial, Temporal, and Spatiotemporal Statistics
plot.vario

Plot vario objects
vario

vario
phase.partnered

Phase partnered time series
latlon2dist

latlon2dist
coord2dist

coord2dist
meancorr

Compute mean column-wise correlation and determine its significance via Monte Carlo randomizations