Package: modeltime.ensemble
Type: Package
Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
Version: 1.1.0.9000
Authors@R: c(
    person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
    person("Business Science", role = "cph")
    )
Description: A 'modeltime' extension that implements time series
        ensemble forecasting methods including model averaging,
        weighted averaging, and stacking. These techniques are popular
        methods to improve forecast accuracy and stability.
URL: https://business-science.github.io/modeltime.ensemble/,
        https://github.com/business-science/modeltime.ensemble
BugReports: https://github.com/business-science/modeltime.ensemble/issues
License: MIT + file LICENSE
Encoding: UTF-8
Depends: modeltime (>= 1.3.3), modeltime.resample (>= 0.3.0), R (>=
        3.5)
Imports: tune (>= 2.0.0), rsample, yardstick, workflows (>= 0.2.1),
        recipes (>= 0.1.15), timetk (>= 2.5.0), tibble, dplyr (>=
        1.0.0), tidyr, purrr, stringr, rlang (>= 0.1.2), cli, generics,
        magrittr, tictoc, parallel, doParallel, foreach, glmnet
Suggests: gt, dials, utils, earth, testthat, tidymodels, xgboost,
        lubridate, knitr, rmarkdown
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libxml2-dev
        libssl-dev libnode-dev libx11-dev
Repository: https://business-science.r-universe.dev
Date/Publication: 2025-12-16 19:16:36 UTC
RemoteUrl: https://github.com/business-science/modeltime.ensemble
RemoteRef: HEAD
RemoteSha: ace8e091e41e1d1040abe1fc94ee873f08365978
NeedsCompilation: no
Packaged: 2026-06-01 07:57:52 UTC; root
Author: Matt Dancho [aut, cre],
  Business Science [cph]
Maintainer: Matt Dancho <mdancho@business-science.io>