# This file created by conda-build 3.21.7
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Fri Jan 21 00:14:44 2022
# ------------------------------------------------

package:
  name: r-sits
  version: 0.16.1
source:
  sha256: df07f4a08630ebe2fcbe685544dd367a90423c7ddcd5fcf4520549358c796d83
  url:
    - https://github.com/e-sensing/sits/archive/refs/tags/v0.16.1.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40h8bdc2d5_0
requirements:
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  host:
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    - r-ggplot2 3.3.5 r40hc72bb7e_0
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    - r-knitr 1.37 r40hc72bb7e_1
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    - ucrt 10.0.20348.0 h57928b3_0
    - udunits2 2.2.27.27 h892ecd3_3
    - vc 14.2 hb210afc_6
    - vs2015_runtime 14.29.30037 h902a5da_6
  run:
    - libblas >=3.8.0,<4.0a0
    - liblapack >=3.8.0,<4.0a0
    - m2w64-gcc-libs
    - r-assertthat
    - r-base >=4.0,<4.1.0a0
    - r-config
    - r-data.table >=1.13
    - r-dplyr >=1.0.0
    - r-furrr
    - r-gdalutilities
    - r-ggplot2
    - r-lubridate >=1.7.0
    - r-magrittr
    - r-purrr >=0.3.0
    - r-rCurl
    - r-raster >=3.4
    - r-reshape2
    - r-rstac
    - r-scales
    - r-sf >=0.9
    - r-slider >=0.1.5
    - r-terra >=1.0.10
    - r-tibble >=3.0
test:
  commands:
    - '"%R%" -e "library(''sits'')"'
about:
  home: https://github.com/e-sensing/sits
  license: GPL-3.0-or-later
  license_family: GPL3
  license_file:
    - D:\bld\r-sits_1642724307426\_h_env/lib/R/share/licenses/GPL-3
    - LICENSE
  summary: 'The sits R package provides a set of tools for analysis, visualization
    and classification of satellite image time series. The main aim of SITS is to
    support land cover and land change classification of image data cubes using machine
    learning methods. The basic workflow in SITS is:


    1. Create a data cube using image collections available in the cloud or in local
    machines.

    2. Extract time series from the data cube which are used as training data.

    3. Perform quality control and filtering on the samples.

    4. Train a machine learning model using the extracted samples.

    5. Classify the data cube using the trained model.

    6. Post-process the classified images.

    7. Evaluate the accuracy of the classification using best practices.

    '
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - TomAugspurger
    - conda-forge/r
