2D Boxcount for 1D signal

```
fd_boxcount2D(
y = NA,
unitSquare = TRUE,
image2D = NA,
resolution = 1,
removeTrend = FALSE,
polyOrder = 1,
standardise = c("none", "mean.sd", "median.mad")[1],
adjustSumOrder = FALSE,
scaleMin = 0,
scaleMax = floor(log2(NROW(y) * resolution)),
scaleS = NA,
dataMin = 2^(scaleMin + 1),
maxData = 2^(scaleMax - 1),
doPlot = FALSE,
returnPlot = FALSE,
returnPLAW = FALSE,
returnInfo = FALSE,
returnLocalScaling = FALSE,
silent = FALSE,
noTitle = FALSE,
tsName = "y"
)
```

- y
A numeric vector or time series object.

- unitSquare
Create unit square image of

`y`

? This is required for estimating FD of time series (default =`TRUE`

)- image2D
A matrix representing a 2D image, argument

`y`

and`unitSquare`

will be ignored (default =`NA`

)- resolution
The resolution used to embed the timeseries in 2D, a factor by which the dimensions the matrix will be multiplied (default =

`1`

)- removeTrend
If

`TRUE`

, will call ts_detrend on`y`

(default =`FALSE`

)- polyOrder
Order of polynomial trend to remove if

`removeTrend =`

TRUE``- standardise
Standardise

`y`

using`ts_standardise()`

with`adjustN = FALSE`

(default =`none`

)- adjustSumOrder
Adjust the order of the time series (by summation or differencing), based on the global scaling exponent, see e.g. https://www.frontiersin.org/files/Articles/23948/fphys-03-00141-r2/image_m/fphys-03-00141-t001.jpgIhlen (2012) (default = `FALSE``)

- scaleMin
Minimium scale value (as

`2^scale`

) to use (default =`0`

)- scaleMax
Maximum scale value (as

`2^scale`

) to use (default =`max`

of`log2(nrows)`

and`log2(ncols)`

)- scaleS
If not

`NA`

, pass a numeric vector listing the scales (as a power of`2`

) on which to evaluate the boxcount. Arguments`scaleMax`

,`scaleMin`

, and`scaleResolution`

will be ignored (default =`NA`

)- dataMin
Minimum number of time/data points inside a box for it to be included in the slope estimation (default =

`2^scaleMin`

)- maxData
Maximum number of time/data points inside a box for it to be included in the slope estimation (default =

`2^scaleMax`

)- doPlot
Return the log-log scale versus bulk plot with linear fit (default =

`TRUE`

).- returnPlot
Return ggplot2 object (default =

`FALSE`

)- returnPLAW
Return the power law data (default =

`FALSE`

)- returnInfo
Return all the data used in DFA (default =

`FALSE`

)- returnLocalScaling
Return estimates of FD for each scale

- silent
Silent-ish mode (default =

`TRUE`

)- noTitle
Do not generate a title (only the subtitle)

- tsName
Name of y added as a subtitle to the plot (default =

`y`

)

The boxcount fractal dimension and the 'local' boxcount fractal dimension

This function was inspired by the `Matlab`

function `boxcount.m`

written by F. Moisy. `Fred Hasselman`

adapted the function for `R`

for the purpose of the unit square boxcount analysis for 1D time series. The original Matlab toolbox has more options and contains more functions (e.g. `1D`

and `3D`

boxcount).

```
fd_boxcount2D(y = rnorm(100))
#>
#>
#> Raterizing time series... Done!
#> Performing 2D boxcount...Done!
#>
#> ~~~o~~o~~casnet~~o~~o~~~
#>
#> 2D boxcount of 1D curve
#>
#> Full range (n = 9)
#> FD = 1.52
#>
#> Exclude scales (n = 7)
#> FD = 1.52
#>
#> ~~~o~~o~~casnet~~o~~o~~~
```