# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Thu May  6 02:25:41 2021
# ------------------------------------------------

package:
  name: r-quanteda.textmodels
  version: 0.9.4
source:
  sha256: 30c06c405ef15c6424a6ba6c184ec97230b496b0b7d60cfe493c663b92a65664
  url:
    - https://cran.r-project.org/src/contrib/Archive/quanteda.textmodels/quanteda.textmodels_0.9.4.tar.gz
    - https://cran.r-project.org/src/contrib/quanteda.textmodels_0.9.4.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r36haf3e08b_0
requirements:
  build:
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    - msys2-conda-epoch 20160418 1
  host:
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    - r-purrr 0.3.4 r36hda5aaf8_1
    - r-quanteda 3.0.0 r36ha856d6a_1
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    - r-rappdirs 0.3.3 r36h6d2157b_0
    - r-rcpp 1.0.6 r36ha856d6a_0
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    - zlib 1.2.11 h62dcd97_1010
  run:
    - libblas >=3.8.0,<4.0a0
    - libcblas >=3.8.0,<4.0a0
    - liblapack >=3.8.0,<4.0a0
    - liblapacke >=3.8.0,<4.0a0
    - m2w64-gcc-libs
    - r-base >=3.6,<3.7.0a0
    - r-glmnet
    - r-liblinear
    - r-matrix >=1.2
    - r-quanteda >=2.0
    - r-rcpp >=0.12.12
    - r-rcpparmadillo >=0.7.600.1.0
    - r-rcppparallel
    - r-rspectra
    - r-sparsem
    - r-stringi
test:
  commands:
    - '"%R%" -e "library(''quanteda.textmodels'')"'
about:
  home: https://github.com/quanteda/quanteda.textmodels
  license: GPL-3.0-only
  license_family: GPL3
  license_file:
    - D:\bld\r-quanteda.textmodels_1620268287269\_h_env/lib/R/share/licenses/GPL-3
  summary: Scaling models and classifiers for sparse matrix objects representing textual
    data in the form of a document-feature matrix.  Includes original implementations
    of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, 'Wordscores'
    model, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model,
    and 'Slapin' and 'Proksch's' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
    model, as well as methods for correspondence analysis, latent semantic analysis,
    and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
    - izahn
