Package: WaveletGBM
Type: Package
Title: Wavelet Based Gradient Boosting Method
Version: 0.1.0
Authors@R: c(person(" Dr. Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
    person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
Author: Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]
Maintainer: Dr. Ranjit Kumar Paul <ranjitstat@gmail.com>
Description: 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>.
License: GPL-3
Encoding: UTF-8
Imports: caret, dplyr, caretForecast, Metrics, tseries, stats,
        wavelets, gbm
RoxygenNote: 7.2.1
NeedsCompilation: no
Packaged: 2026-10-13 12:16:43 UTC; root
Config/pak/sysreqs: libicu-dev libssl-dev
Repository: https://ranjitstat.r-universe.dev
Date/Publication: 2023-04-07 07:20:02 UTC
RemoteUrl: https://github.com/cran/WaveletGBM
RemoteRef: HEAD
RemoteSha: 3639d59be48c8c35bd44ec754053d6480494170d