Package: WaveletGBM 0.1.0

WaveletGBM: Wavelet Based Gradient Boosting Method

Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>.

Authors:Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]

WaveletGBM_0.1.0.tar.gz
WaveletGBM_0.1.0.zip(r-4.7-any)WaveletGBM_0.1.0.zip(r-4.6-any)WaveletGBM_0.1.0.zip(r-4.5-any)
WaveletGBM_0.1.0.tgz(r-4.6-any)WaveletGBM_0.1.0.tgz(r-4.5-any)
WaveletGBM_0.1.0.tar.gz(r-4.7-any)WaveletGBM_0.1.0.tar.gz(r-4.6-any)
WaveletGBM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
WaveletGBM/json (API)

# Install 'WaveletGBM' in R:

install.packages('WaveletGBM', repos = c('https://ranjitstat.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

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

1.00 score 253 downloads 1 exports 91 dependencies

Last updated from:3639d59be4. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	182
source / vignettes	OK	210
linux-release	OK	170
macos-release	OK	160
macos-oldrel	OK	130
windows-devel	OK	147
windows-release	OK	110
windows-oldrel	OK	118
wasm-release	OK	112