CRAN/E | smoots

smoots

Nonparametric Estimation of the Trend and Its Derivatives in TS

Installation

About

The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary 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. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) doi:10.1080/10485252.2020.1759598.

wiwi.uni-paderborn.de/en/dep4/feng/ https://wiwi.uni-paderborn.de/dep4/gries/
wiwi.uni-paderborn.de/en/dep4/feng/ https://wiwi.uni-paderborn.de/dep4/gries/

Key Metrics

Version 1.1.4
R ≥ 2.10
Published 2023-09-11 397 days ago
Needs compilation? yes
License GPL-3
CRAN checks smoots results

Downloads

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Last 7 days 79 -18%
Last 30 days 353 -13%
Last 90 days 1.080 -43%
Last 365 days 6.075 +7%

Maintainer

Maintainer

Dominik Schulz

Authors

Yuanhua Feng

aut

(Paderborn University, Germany)

Sebastian Letmathe

aut

(Paderborn University, Germany)

Dominik Schulz

aut / cre

(Paderborn University, Germany)

Thomas Gries

ctb

(Paderborn University, Germany)

Marlon Fritz

ctb

(Paderborn University, Germany)

Material

README
NEWS
Reference manual
Package source

In Views

TimeSeries

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

smoots archive

Depends

R ≥ 2.10

Imports

stats
utils
graphics
grDevices
Rcpp ≥ 1.0.7
future ≥1.22.1
future.apply ≥ 1.8.1
progressr ≥ 0.8.0
progress ≥ 1.2.2

Suggests

knitr
rmarkdown
fGarch
RcppArmadillo ≥ 0.10.6.0.0
testthat ≥ 3.0.0

LinkingTo

Rcpp
RcppArmadillo

Reverse Imports

esemifar
quarks
ufRisk