{
  "channels": [
    "conda-forge",
    "defaults"
  ],
  "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": [
      "johanneskoester",
      "bgruening",
      "daler",
      "jdblischak",
      "cbrueffer",
      "dbast"
    ]
  },
  "home": "https://github.com/paul-buerkner/brms, http://discourse.mc-stan.org",
  "identifiers": [],
  "keywords": [],
  "license": "GPL (>= 3)",
  "license_family": "GPL3",
  "license_file": "C:\\bld\\r-brms_1541580807409\\_h_env\\R\\share\\licenses\\GPL-3",
  "root_pkgs": [
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    "ca-certificates 2018.10.15 ha4d7672_0",
    "certifi 2018.10.15 py36_1000",
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    "conda-env 2.6.0 h36134e3_1",
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    "python 3.6.5 h0c2934d_0",
    "python-dateutil 2.7.5 py_0",
    "pytz 2018.7 py_0",
    "pywin32 223 py36hfa6e2cd_1",
    "pyyaml 3.13 py36hfa6e2cd_1001",
    "requests 2.18.4 py36h4371aae_1",
    "ruamel_yaml 0.15.37 py36hfa6e2cd_2",
    "setuptools 39.2.0 py36_0",
    "six 1.11.0 py36h4db2310_1",
    "tqdm 4.28.1 py_0",
    "traitlets 4.3.2 py36_1000",
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    "vc 14.1 h0510ff6_4",
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    "yaml 0.1.7 vc14_0"
  ],
  "summary": "Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions  and link functions are supported, allowing users to fit -- among others --  linear, robust linear, count data, survival, response times, ordinal,  zero-inflated, hurdle, and even self-defined mixture models all in a  multilevel context. Further modeling options include non-linear and  smooth terms, auto-correlation structures, censored data, meta-analytic  standard errors, and quite a few more. In addition, all parameters of the  response distribution can be predicted in order to perform distributional  regression. Prior specifications are flexible and explicitly encourage  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: B\u00fcrkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.",
  "tags": []
}