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.

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

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

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

function.

```
library(colorednoise)
set.seed(7927)
<- matrix_model(
pop_blue 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)
),
)<- matrix_model(
pop_red 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)`