{% set version = '0.1.2' %}

{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

package:
  name: r-timetk
  version: {{ version|replace("-", "_") }}

source:
  url:
    - {{ cran_mirror }}/src/contrib/timetk_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/timetk/timetk_{{ version }}.tar.gz
  sha256: 8a4c52534535389aed1fbeac9d5a2536351249c6761c4cac15b131dcbcbce736

build:
  merge_build_host: True  # [win]
  number: 0

  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{posix}}zip               # [win]

  host:
    - r-base
    - 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

  run:
    - r-base
    - 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')"           # [not win]
    - "\"%R%\" -e \"library('timetk')\""  # [win]

about:
  home: https://github.com/business-science/timetk
  license: 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>.'

  license_family: GPL3
  license_file:
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

extra:
  recipe-maintainers:
    - conda-forge/r
    - oblute
    - rluria14
    - benhuff

# The original CRAN metadata for this package was:

# Package: timetk
# Type: Package
# Title: A Tool Kit for Working with Time Series
# Version: 0.1.2
# Date: 2019-09-21
# 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: 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>.
# 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: dplyr (>= 0.7.0), 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)
# Suggests: tidyverse, broom, forcats, forecast, knitr, rmarkdown, robets, scales, stringr, testthat, tidyquant, fracdiff, timeDate, timeSeries, tseries
# RoxygenNote: 6.1.1
# VignetteBuilder: knitr
# NeedsCompilation: no
# Packaged: 2019-09-25 10:53:22 UTC; mdancho
# Author: Matt Dancho [aut, cre], Davis Vaughan [aut]
# Maintainer: Matt Dancho <mdancho@business-science.io>
# Repository: CRAN
# Date/Publication: 2019-09-25 13:50:02 UTC

# See
# https://docs.conda.io/projects/conda-build for
# more information about meta.yaml
