Package: colorednoise 1.1.2

July Pilowsky

colorednoise: Simulate Temporally Autocorrelated Populations

Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.

Authors:July Pilowsky [aut, cre]

colorednoise_1.1.2.tar.gz
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colorednoise_1.1.2.tgz(r-4.4-emscripten)colorednoise_1.1.2.tgz(r-4.3-emscripten)
colorednoise.pdf |colorednoise.html✨
colorednoise/json (API)
NEWS

# Install 'colorednoise' in R:
install.packages('colorednoise', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/japilo/colorednoise/issues0 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

2.70 score 356 downloads 1 mentions 9 exports 10 dependencies

Last updated 1 years agofrom:93c8eaa13c. Checks:2 OK, 1 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-linux-x86_64NOTEMar 25 2025
R-4.4-linux-x86_64OKMar 25 2025

Exports:autocorr_simautocorrelationcolored_multi_rnormcolored_noisecor2covmatrix_modelmulti_rnormstdev_transformunstructured_pop

Dependencies:clidata.tablegluelifecyclemagrittrpurrrRcppRcppArmadillorlangvctrs

Estimating Autocorrelation of Colored Noise

Julia Pilowsky

Rendered fromnoise.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2024-02-29
Started: 2017-10-10

Citation

To cite package ‘colorednoise’ in publications use:

Pilowsky J (2024). colorednoise: Simulate Temporally Autocorrelated Populations. R package version 1.1.2, https://CRAN.R-project.org/package=colorednoise.

Corresponding BibTeX entry: 

  @Manual{,
    title = {colorednoise: Simulate Temporally Autocorrelated
      Populations},
    author = {July Pilowsky},
    year = {2024},
    note = {R package version 1.1.2},
    url = {https://CRAN.R-project.org/package=colorednoise},
  }

Readme and manuals

colorednoise

Overview

Many populations that change over time are temporally autocorrelated, which means that the random noise in each timestep is correlated to that of the previous timestep. Instead of uncorrelated white noise, these populations are governed by blue noise (negatively autocorrelated) or red noise (positively autocorrelated.)

The colorednoise package allows you to simulate colored noise as well as populations whose behavior is governed by colored noise.

Installation

You can install the latest version of colorednoise from github with:

# install.packages("devtools")
devtools::install_github("japilo/colorednoise")

Example

Here are plots of blue- and red-noise populations generated by the matrix_model function.

library(colorednoise)
set.seed(7927)
pop_blue <- matrix_model(
  data = list(
    mean = matrix(c(0.6687097, 0.2480645, 0.6687097, 0.4335484), ncol=2),
    sd = matrix(c(0.34437133, 0.08251947, 0.34437133, 0.10898160), ncol=2),
    autocorrelation = matrix(rep(-0.4, 4), ncol=2)
  ), timesteps = 100, initialPop = c(100, 100)
)
pop_red <- matrix_model(
  data = list(
    mean = matrix(c(0.6687097, 0.2480645, 0.6687097, 0.4335484), ncol=2),
    sd = matrix(c(0.34437133, 0.08251947, 0.34437133, 0.10898160), ncol=2),
    autocorrelation = matrix(rep(0.4, 4), ncol=2)
  ), timesteps = 100, initialPop = c(100, 100)
)
ggplot(pop_blue, aes(x = timestep, y = total)) + geom_line(col="blue") + ylim(0, 6000)


ggplot(pop_red, aes(x = timestep, y = total)) + geom_line(col="red") + ylim(0, 6000)