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-4.5)WaveletGBM_0.1.0.zip(r-4.4)WaveletGBM_0.1.0.zip(r-4.3)
WaveletGBM_0.1.0.tgz(r-4.4-any)WaveletGBM_0.1.0.tgz(r-4.3-any)
WaveletGBM_0.1.0.tar.gz(r-4.5-noble)WaveletGBM_0.1.0.tar.gz(r-4.4-noble)
WaveletGBM_0.1.0.tgz(r-4.4-emscripten)WaveletGBM_0.1.0.tgz(r-4.3-emscripten)
WaveletGBM.pdf |WaveletGBM.html✨
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 144 downloads 1 exports 92 dependencies

Last updated 2 years agofrom:3639d59be4. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 29 2024
R-4.5-win	OK	Oct 29 2024
R-4.5-linux	OK	Oct 29 2024
R-4.4-win	OK	Oct 29 2024
R-4.4-mac	OK	Oct 29 2024
R-4.3-win	OK	Oct 29 2024
R-4.3-mac	OK	Oct 29 2024