The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The IMW-package implements a method for calculating the
rolling statistics mean, variance, skewness, and kurtosis. Furthermore,
it provides a function for updating these statistics as new information
is received, allowing for online/batch calculation of these
statistics.
The IMW-package depends on R (>= 4.0.1)
and Rcpp (>= 1.0.4.6). As the package is not available
on CRAN, remotes is needed to install the package from
github.
From R, run the following commands:
install.packages("Rcpp")
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("svilsen/IMW")A series of 100 observations is generated randomly, and the rolling mean, variance, skewness, and kurtosis are calculated using a window size of 10.
## Initial set-up
#
set.seed(1234)
# Observed data
N <- 100
x <- cumsum(rnorm(N))
# Window size
k <- 10
# Calculating rolling statistics using window size 'k'
mw <- imw(x, k)
#
plot(mw, xlim = c(1, 150))Subsequently, a new batch of 50 observations are observed, and the rolling statistics are updated given this additional information.
## Updating moments
# Additional data
M <- 50
y <- cumsum(c(tail(x, 1), rnorm(M)))[-1]
# Update the moments using new information
umw <- uimw(mw, y)
#
plot(umw, xlim = c(1, 150))This project is licensed under the MIT License.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.