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., (2021) <>.

Version: 1.0.2
Depends: R (≥ 2.10)
Imports: fracdiff, stats, smoots, graphics, grDevices
Published: 2023-10-22
Author: Yuanhua Feng [aut] (Paderborn University, Germany), Jan Beran [aut] (University of Konstanz, Germany), Sebastian Letmathe [aut, cre] (Paderborn University, Germany), Dominik Schulz [aut] (Paderborn University, Germany)
Maintainer: Sebastian Letmathe <sebastian.letmathe at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: esemifar results


Reference manual: esemifar.pdf


Package source: esemifar_1.0.2.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): esemifar_1.0.2.tgz, r-release (arm64): esemifar_1.0.2.tgz, r-oldrel (arm64): esemifar_1.0.2.tgz, r-prerel (x86_64): esemifar_1.0.2.tgz, r-release (x86_64): esemifar_1.0.2.tgz
Old sources: esemifar archive

Reverse dependencies:

Reverse imports: ufRisk


Please use the canonical form to link to this page.