Removes directional signals from time series using loess, linear regression or gaussian detrending.

## Arguments

- data
The dataframe to be detrended. The first column must be a vector of dates with all other columns the individual time series.

- method
The method of detrending. Options include

`"linear"`

(residuals of a linear regression),`loess`

(smoothing by local polynomial regression),`gaussian`

(smoothing by a gaussian kernel), or`first.difference`

.- bandwidth
If

`method = "gaussian"`

, dictates the bandwidth of the gaussian kernel. If`NULL`

, this is estimated from the data.- span
If

`method = "loess"`

, controls the degree of smoothing as a proportion of points to be used (if`span = 1`

, all points are used)- degree
If

`method = "loess"`

, specifies the degree polynomials allowed. Options are normally`1`

or`2`

.

## Examples

```
#Generate five random monthly time series
#of 5 years length.
spp_data <- matrix(nrow = 5*12, ncol = 5)
spp_data <- sapply(1:dim(spp_data)[2], function(x){
spp_data[,x] <- rnorm(5*12,mean=20,sd=5)})
multi_spp_data <- cbind("time" =
seq(as.Date('2000/01/01'), as.Date('2004/12/01'), by="month"),
as.data.frame(spp_data))
detrend_dat <- detrend_ts(data = multi_spp_data,
method = "gaussian",
bandwidth = 2)
```