# This file created by conda-build 3.18.11
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Tue Feb 18 20:38:11 2020
# ------------------------------------------------

package:
    name: r-timetk
    version: 0.1.2
source:
    sha256: 8a4c52534535389aed1fbeac9d5a2536351249c6761c4cac15b131dcbcbce736
    url:
        - https://cran.r-project.org/src/contrib/Archive/timetk/timetk_0.1.2.tar.gz
        - https://cran.r-project.org/src/contrib/timetk_0.1.2.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r35h6115d3f_0
requirements:
    build:
        - m2-gcc-libs 5.3.0 4
        - m2-libbz2 1.0.6 3
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2-zip 3.0 2
        - msys2-conda-epoch 20160418 1
    host:
        - _r-mutex 1.0.1 anacondar_1
        - m2w64-bwidget 1.9.10 2
        - m2w64-bzip2 1.0.6 6
        - m2w64-expat 2.1.1 2
        - m2w64-fftw 3.3.4 6
        - m2w64-flac 1.3.1 3
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2w64-gcc-libs 5.3.0 7
        - m2w64-gcc-libs-core 5.3.0 7
        - m2w64-gettext 0.19.7 2
        - m2w64-gmp 6.1.0 2
        - m2w64-gsl 2.1 2
        - m2w64-icu 58.2 heb44b8b_2
        - m2w64-libiconv 1.14 6
        - m2w64-libjpeg-turbo 1.4.2 3
        - m2w64-libogg 1.3.2 3
        - m2w64-libpng 1.6.21 2
        - m2w64-libsndfile 1.0.26 2
        - m2w64-libtiff 4.0.6 2
        - m2w64-libvorbis 1.3.5 2
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-libxml2 2.9.3 4
        - m2w64-mpfr 3.1.4 4
        - m2w64-openblas 0.2.19 1
        - m2w64-pcre 8.38 2
        - m2w64-speex 1.2rc2 3
        - m2w64-speexdsp 1.2rc3 3
        - m2w64-tcl 8.6.5 3
        - m2w64-tk 8.6.5 3
        - m2w64-tktable 2.10 5
        - m2w64-wineditline 2.101 5
        - m2w64-xz 5.2.2 2
        - m2w64-zlib 1.2.8 10
        - msys2-conda-epoch 20160418 1
        - r-assertthat 0.2.1 r35h6115d3f_1
        - r-backports 1.1.5 r35hda5aaf8_0
        - r-base 3.5.1 h0c8e037_1012
        - r-cli 2.0.1 r35h6115d3f_0
        - r-clipr 0.7.0 r35h6115d3f_0
        - r-crayon 1.3.4 r35h6115d3f_1002
        - r-digest 0.6.24 r35h796a38f_0
        - r-dplyr 0.8.4 r35h796a38f_0
        - r-ellipsis 0.3.0 r35hda5aaf8_0
        - r-fansi 0.4.1 r35hda5aaf8_0
        - r-glue 1.3.1 r35hda5aaf8_1
        - r-hms 0.5.3 r35h6115d3f_0
        - r-lattice 0.20_38 r35hda5aaf8_1002
        - r-lazyeval 0.2.2 r35hda5aaf8_1
        - r-lifecycle 0.1.0 r35h6115d3f_0
        - r-lubridate 1.7.4 r35h796a38f_1002
        - r-magrittr 1.5 r35h6115d3f_1002
        - r-padr 0.5.0 r35h796a38f_1
        - r-pillar 1.4.3 r35h6115d3f_0
        - r-pkgconfig 2.0.3 r35h6115d3f_0
        - r-plogr 0.2.0 r35h6115d3f_1002
        - r-purrr 0.3.3 r35hda5aaf8_0
        - r-r6 2.4.1 r35h6115d3f_0
        - r-rcpp 1.0.3 r35h796a38f_0
        - r-readr 1.3.1 r35h796a38f_1002
        - r-rlang 0.4.4 r35hda5aaf8_0
        - r-stringi 1.4.6 r35h796a38f_0
        - r-stringr 1.4.0 r35h6115d3f_1
        - r-tibble 2.1.3 r35hda5aaf8_1
        - r-tidyr 1.0.2 r35h796a38f_0
        - r-tidyselect 1.0.0 r35h6115d3f_0
        - r-utf8 1.1.4 r35hda5aaf8_1001
        - r-vctrs 0.2.2 r35hda5aaf8_0
        - r-xts 0.12_0 r35hda5aaf8_0
        - r-zeallot 0.1.0 r35h6115d3f_1001
        - r-zoo 1.8_7 r35hda5aaf8_0
    run:
        - r-base >=3.5,<3.6.0a0
        - r-dplyr >=0.7.0
        - r-lazyeval >=0.2.0
        - r-lubridate >=1.6.0
        - r-padr >=0.3.0
        - r-purrr >=0.2.2
        - r-readr >=1.0.0
        - r-stringi >=1.1.5
        - r-tibble >=1.2
        - r-tidyr >=0.6.1
        - r-xts >=0.9_7
        - r-zoo >=1.7_14
test:
    commands:
        - '"%R%" -e "library(''timetk'')"'
about:
    home: https://github.com/business-science/timetk
    license: GPL-3
    license_family: GPL3
    license_file:
        - D:\bld\r-timetk_1582058486378\_h_env/lib/R/share/licenses/GPL-3
    summary: 'Get the time series index (date or date-time component), time series
        signature (feature extraction of date or date-time component for time series
        machine learning), and time series summary (summary attributes about time
        series). Create future time series based on properties of existing time series
        index using logistic regression. Coerce between time-based tibbles (''tbl'')
        and ''xts'', ''zoo'', and ''ts''. 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>.'
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - benhuff
        - conda-forge/r
        - oblute
        - rluria14
