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_2.2.1.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-tidyselect:single:standard
40 R-slider:single:standard
41 R-anytime:single:standard
42 R-timeDate:single:standard
43 R-forecast:single:standard
45 R-assertthat:single:standard
46 R-generics:single:standard
54 INSTALL_REQ_TOOLCHAIN= yes
56 [FILE:591:descriptions/desc.single]
57 timetk: A Tool Kit for Working with Time Series in R
59 Easy visualization, wrangling, and feature engineering of time series data
60 for forecasting and machine learning prediction. Methods discussed herein
61 are commonplace in machine learning, and have been cited in various
62 literature. Refer to "Calendar Effects" in papers such as Taieb, Souhaib
63 Ben. "Machine learning strategies for multi-step-ahead time series
64 forecasting." Universit Libre de Bruxelles, Belgium (2014): 75-86. <<a
65 href="http://souhaib-bentaieb.com/pdf/2014_phd.pdf">http://souhaib-bentaieb.com/pdf/2014_phd.pdf</a>>.
69 a83a48c0988e7bae79d3c72b9636efb3b6c3b61c6ecf5b8439431818b2e10f54 4271936 CRAN/timetk_2.2.1.tar.gz