Package `runstats`

provides methods for fast computation of running sample statistics for time series. The methods utilize Convolution Theorem to compute convolutions via Fast Fourier Transform (FFT). Implemented running statistics include:

- mean,
- standard deviation,
- variance,
- covariance,
- correlation,
- euclidean distance.

```
library(runstats)
## Example: running correlation
x0 <- sin(seq(0, 2 * pi * 5, length.out = 1000))
x <- x0 + rnorm(1000, sd = 0.1)
pattern <- x0[1:100]
out1 <- RunningCor(x, pattern)
out2 <- RunningCor(x, pattern, circular = TRUE)
## Example: running mean
x <- cumsum(rnorm(1000))
out1 <- RunningMean(x, W = 100)
out2 <- RunningMean(x, W = 100, circular = TRUE)
```

To better explain the details of running statistics, package’s function `runstats.demo(func.name)`

allows to visualize how the output of each running statistics method is generated. To run the demo, use `func.name`

being one of the methods’ names:

`"RunningMean"`

,`"RunningSd"`

,`"RunningVar"`

,`"RunningCov"`

,`"RunningCor"`

,`"RunningL2Norm"`

.