1 # Buildsheet autogenerated by ravenadm tool -- Do not edit.
7 SDESC[standard]= Tool Kit for Working with Time Series in R
8 HOMEPAGE= https://github.com/business-science/timetk
9 CONTACT= CRAN_Automaton[cran@ironwolf.systems]
12 SITES[main]= CRAN/src/contrib
13 DISTFILE[1]= timetk_2.0.0.tar.gz:main
16 SPKGS[standard]= single
18 OPTIONS_AVAILABLE= none
19 OPTIONS_STANDARD= none
21 BUILDRUN_DEPENDS= R-recipes:single:standard
22 R-rsample:single:standard
23 R-dplyr:single:standard
24 R-ggplot2:single:standard
25 R-forcats:single:standard
26 R-stringr:single:standard
27 R-plotly:single:standard
28 R-lazyeval:single:standard
29 R-lubridate:single:standard
30 R-padr:single:standard
31 R-purrr:single:standard
32 R-readr:single:standard
33 R-stringi:single:standard
34 R-tibble:single:standard
35 R-tidyr:single:standard
38 R-rlang:single:standard
39 R-slider:single:standard
40 R-anytime:single:standard
41 R-timeDate:single:standard
42 R-forecast:single:standard
44 R-assertthat:single:standard
52 INSTALL_REQ_TOOLCHAIN= yes
54 [FILE:585:descriptions/desc.single]
55 timetk: A Tool Kit for Working with Time Series in R
57 Easy visualization, wrangling, and preprocessing of time series data for
58 forecasting and machine learning prediction. Methods discussed herein are
59 commonplace in machine learning, and have been cited in various literature.
60 Refer to "Calendar Effects" in papers such as Taieb, Souhaib Ben. "Machine
61 learning strategies for multi-step-ahead time series forecasting."
62 Universit Libre de Bruxelles, Belgium (2014): 75-86. <<a
63 href="http://souhaib-bentaieb.com/pdf/2014_phd.pdf">http://souhaib-bentaieb.com/pdf/2014_phd.pdf</a>>.
67 2421bc76cc0359614a66203810756cbd61ba235e7ea29467ec9c89905049a942 5014826 CRAN/timetk_2.0.0.tar.gz