Package: DCSmooth 1.1.2
Bastian Schaefer
DCSmooth: Nonparametric Regression and Bandwidth Selection for Spatial Models
Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.
Authors:
DCSmooth_1.1.2.tar.gz
DCSmooth_1.1.2.tar.gz(r-4.5-noble)DCSmooth_1.1.2.tar.gz(r-4.4-noble)
DCSmooth_1.1.2.tgz(r-4.4-emscripten)DCSmooth_1.1.2.tgz(r-4.3-emscripten)
DCSmooth.pdf |DCSmooth.html✨
DCSmooth/json (API)
NEWS
| # Install 'DCSmooth' in R: |
| install.packages('DCSmooth', repos = 'https://cloud.r-project.org') |
- returns.alv - Returns of Allianz SE
- temp.nunn - Temperatures from Nunn, CO
- temp.yuma - Temperatures from Yuma, AZ
- volumes.alv - Volumes of Allianz SE
- wind.nunn - Wind Speed from Nunn, CO
- wind.yuma - Wind Speed from Yuma, AZ
- y.norm1 - Single Gaussian Peak
- y.norm2 - Double Gaussian Peak
- y.norm3 - Double Gaussian Ridges
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:a26fd36e45. Checks:1 OK, 2 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 04 2025 |
| R-4.5-linux-x86_64 | NOTE | Mar 04 2025 |
| R-4.4-linux-x86_64 | NOTE | Mar 04 2025 |
Exports:dcskernel.assignkernel.listqarma.estqarma.simsarma.estsarma.simset.optionssfarima.estsfarima.simsurface.dcs
Dependencies:askpassbase64encbslibcachemclicodetoolscolorspacecpp11crosstalkcurldata.tabledigestdoParalleldplyrevaluatefansifarverfastmapfontawesomeforeachfracdifffsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangrmarkdownsassscalesstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
Citation
To cite package ‘DCSmooth’ in publications use:
Schaefer B (2021). DCSmooth: Nonparametric Regression and Bandwidth Selection for Spatial Models. R package version 1.1.2, https://CRAN.R-project.org/package=DCSmooth.
Corresponding BibTeX entry:
@Manual{,
title = {DCSmooth: Nonparametric Regression and Bandwidth Selection
for Spatial Models},
author = {Bastian Schaefer},
year = {2021},
note = {R package version 1.1.2},
url = {https://CRAN.R-project.org/package=DCSmooth},
}
Readme and manuals
DCSmooth
Overview
DCSmooth provides functions for fast nonparametric estimation of the trend surface of a functional or spatial model. Regression types included are kernel regression and local polynomial regression. An automatic bandwidth selector is included.
Installation
You can install the released version of DCSmooth from CRAN with:
install.packages("DCSmooth")
## Example
This basic example shows the two-dimensional estimation of a single gaussian peak
function (in `y.norm1`).
```r
library(DCSmooth)
# simulated data
y = y.norm1 + matrix(rnorm(101^2), nrow = 101, ncol = 101)
# smooth trend surface
y_dcs = dcs(y)
plot(y_dcs, plot_choice = 2)
# surface.dcs(y_dcs, plot_choice = 2)
Details
For a detailed description, see the vignette of this package.
vignette("DCSmooth")
