yardstick (>= 0.0.8), workflows (>= 0.1.3), hardhat, rlang (>=
0.1.2), glue, plotly, reactable, gt, ggplot2, tibble, tidyr,
dplyr, purrr, stringr, forcats, scales, janitor, progressr,
Suggests: rstan, slider, tidymodels, recipes, rsample, tune, tidyverse,

Imports: StanHeaders, timetk (>= 2.6.0), parsnip (>= 0.1.6), dials,
dplyr, purrr, stringr, forcats, scales, janitor, parallel,
doParallel, foreach, magrittr, forecast, xgboost (>= 1.2.0.1),
Suggests: rstan, slider, tidymodels, workflowsets, recipes, rsample,
tune, tidyverse, lubridate, progress, testthat, roxygen2,
kernlab, earth, randomForest, tidyquant, knitr, rmarkdown,

kernlab, thief, earth, randomForest, tidyquant, knitr,

rmarkdown (>= 2.9), webshot, qpdf, covr, TSrepr

Suggests: rstan, slider, sparklyr, tidymodels, workflowsets, recipes,
rsample, tune, tidyverse, lubridate, progress, testthat,
roxygen2, kernlab, thief, smooth, greybox, earth, randomForest,
tidyquant, knitr, rmarkdown (>= 2.9), webshot, qpdf, covr,

roxygen2, kernlab, glmnet, thief, smooth, greybox, earth,
randomForest, tidyquant, knitr, rmarkdown (>= 2.9), webshot,

Imports: StanHeaders, timetk (>= 2.8.0), parsnip (>= 0.2.1), dials,
rsample, tune (>= 0.2.0), tidyverse, lubridate, progress,
testthat, roxygen2, kernlab, glmnet, thief, smooth, greybox,
earth, randomForest, tidyquant, trelliscopejs, knitr, rmarkdown

Imports: StanHeaders, timetk (>= 2.8.1), parsnip (>= 0.2.1), dials,
parallelly, doParallel, foreach, magrittr, forecast, xgboost

rlang (>= 0.1.2), glue, plotly, reactable, gt, ggplot2, tibble,
tidyr, dplyr, purrr, stringr, forcats, scales, janitor,
parallel, parallelly, doParallel, foreach, magrittr, forecast,
xgboost (>= 1.2.0.1), prophet, methods, cli

Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
person("Business Science", role = "cph")
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. Refer to papers such as
"Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
Imports: tune, rsample, yardstick, workflows, parsnip, recipes, dials,
timetk (>= 2.3.0), tibble, dplyr, tidyr, purrr, glue, stringr,
rlang (>= 0.1.2), cli, crayon, utils, generics, magrittr,
Suggests: roxygen2, testthat, tidymodels, tidyverse, lubridate, knitr,