The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
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[@]metanet.ch.

esemifar: Smoothing Long-Memory Time Series

The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.

Version: 2.0.1
Depends: R (≥ 2.10)
Imports: fracdiff, stats, utils, smoots, graphics, grDevices, Rcpp, future, furrr, ggplot2
LinkingTo: Rcpp, RcppArmadillo
Published: 2024-05-07
DOI: 10.32614/CRAN.package.esemifar
Author: Yuanhua Feng [aut] (Paderborn University, Germany), Jan Beran [aut] (University of Konstanz, Germany), Sebastian Letmathe [aut] (Paderborn University, Germany), Dominik Schulz [aut, cre] (Paderborn University, Germany)
Maintainer: Dominik Schulz <dominik.schulz at uni-paderborn.de>
License: GPL-3
URL: https://wiwi.uni-paderborn.de/en/dep4/feng/
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: esemifar results

Documentation:

Reference manual: esemifar.pdf

Downloads:

Package source: esemifar_2.0.1.tar.gz
Windows binaries: r-devel: esemifar_2.0.1.zip, r-release: esemifar_2.0.1.zip, r-oldrel: esemifar_2.0.1.zip
macOS binaries: r-release (arm64): esemifar_2.0.1.tgz, r-oldrel (arm64): esemifar_2.0.1.tgz, r-release (x86_64): esemifar_2.0.1.tgz, r-oldrel (x86_64): esemifar_2.0.1.tgz
Old sources: esemifar archive

Reverse dependencies:

Reverse imports: ufRisk

Linking:

Please use the canonical form https://CRAN.R-project.org/package=esemifar to link to this page.

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.