{
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  ],
  "conda_build_version": "3.15.1",
  "conda_private": false,
  "conda_version": "4.5.11",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "final": true,
    "recipe-maintainers": [
      "dpryan79",
      "cbrueffer",
      "johanneskoester",
      "bgruening",
      "daler",
      "jdblischak"
    ]
  },
  "home": "https://CRAN.R-project.org/package=robCompositions",
  "identifiers": [],
  "keywords": [],
  "license": "GPL (>= 2)",
  "license_family": "GPL3",
  "license_file": "C:\\bld\\r-robcompositions_1540302907693\\_h_env\\R\\share\\licenses\\GPL-3",
  "root_pkgs": [
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  "summary": "Methods for analysis of compositional data including robust methods, imputation, methods to replace rounded zeros, (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.",
  "tags": []
}