Package: timetk
Type: Package
Title: A Tool Kit for Working with Time Series in R
Version: 1.0.0
Authors@R: c(
    person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
    person("Davis", "Vaughan", email = "dvaughan@business-science.io", role = c("aut"))
  )
Description: 
    Easy visualization, wrangling, and preprocessing of time series data for 
    forecasting and machine learning prediction. 
    Methods discussed herein are commonplace in machine learning, and have been cited 
    in various literature. Refer to "Calendar Effects" in papers such as 
    Taieb, Souhaib Ben. "Machine learning strategies for multi-step-ahead time series 
    forecasting." Universit Libre de Bruxelles, Belgium (2014): 75-86. 
    <http://souhaib-bentaieb.com/pdf/2014_phd.pdf>.
URL: https://github.com/business-science/timetk
BugReports: https://github.com/business-science/timetk/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.3.0)
Imports: recipes (>= 0.1.4), rsample, broom, dplyr (>= 0.7.0), ggplot2,
        forcats, stringr, plotly, lazyeval (>= 0.2.0), lubridate (>=
        1.6.0), padr (>= 0.3.0), purrr (>= 0.2.2), readr (>= 1.0.0),
        stringi (>= 1.1.5), tibble (>= 1.2), tidyr (>= 0.6.1), xts (>=
        0.9-7), zoo (>= 1.7-14), rlang (>= 0.1.2), slider, timeDate,
        forecast, hms, assertthat
Suggests: tidyquant, workflows, parsnip, yardstick, tidyverse, knitr,
        rmarkdown, robets, scales, testthat, fracdiff, timeSeries,
        tseries, roxygen2
RoxygenNote: 7.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-04-19 16:58:57 UTC; mdancho
Author: Matt Dancho [aut, cre],
  Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho@business-science.io>
Repository: CRAN
Date/Publication: 2020-04-19 17:50:02 UTC
Built: R 3.5.3; ; 2020-04-22 00:11:15 UTC; windows
