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)WaveletGBM_0.1.0.zip(r-4.6)WaveletGBM_0.1.0.zip(r-4.5)
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✨
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 171 downloads 1 exports 91 dependencies

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

Target	Result	Time
linux-devel-x86_64	OK	172
source / vignettes	OK	190
linux-release-x86_64	OK	171
macos-release-arm64	OK	130
macos-oldrel-arm64	OK	145
windows-devel	OK	128
windows-release	OK	118
windows-oldrel	OK	123
wasm-release	OK	124