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-4.5-any)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 208 downloads 1 exports 92 dependencies

Last updated 2 years agofrom:3639d59be4. Checks:8 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Jan 27 2025
R-4.5-win	OK	Jan 27 2025
R-4.5-mac	OK	Jan 27 2025
R-4.5-linux	OK	Jan 27 2025
R-4.4-win	OK	Jan 27 2025
R-4.4-mac	OK	Jan 27 2025
R-4.3-win	OK	Jan 27 2025
R-4.3-mac	OK	Jan 27 2025