Package: esemifar 2.0.1

Dominik Schulz

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>.

Authors:Yuanhua Feng [aut], Jan Beran [aut], Sebastian Letmathe [aut], Dominik Schulz [aut, cre]

esemifar_2.0.1.tar.gz
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esemifar.pdf |esemifar.html✨
esemifar/json (API)
NEWS

# Install 'esemifar' in R:
install.packages('esemifar', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • airLDN - Daily Observations of the Air Quality Index of London
  • gdpG7 - Quarterly G7 GDP, Q1 1962 to Q4 2019

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.48 score 1 stars 1 packages 1 scripts 298 downloads 10 exports 46 dependencies

Last updated 7 months agofrom:39efaf28b5. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-linux-x86_64OKNov 04 2024

Exports:arma_to_ararma_to_macritMatlmd_to_coefdsmoothlmesemifarfarima_to_arfarima_to_magsmoothtsmoothlm

Dependencies:clicodetoolscolorspacecrayondigestfansifarverfracdifffurrrfuturefuture.applyggplot2globalsgluegtablehmsisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigprettyunitsprogressprogressrpurrrR6RColorBrewerRcppRcppArmadillorlangscalessmootstibbleutf8vctrsviridisLitewithr