{
  "channels": [
    "conda-forge",
    "defaults"
  ],
  "conda_build_version": "3.10.9",
  "conda_private": false,
  "conda_version": "4.5.8",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "home": "https://github.com/strengejacke/ggeffects, https://strengejacke.github.io/ggeffects",
  "license": "GPL-3",
  "license_family": "GPL3",
  "license_file": "C:\\bld\\r-ggeffects_1532086994974\\_h_env\\R\\share\\licenses\\GPL-3",
  "root_pkgs": [
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    "python 3.6.5 h0c2934d_0",
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    "pytz 2018.5 py_0",
    "pywin32 223 py36hfa6e2cd_0",
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    "ruamel_yaml 0.15.35 py36hfa6e2cd_1",
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    "urllib3 1.22 py36h276f60a_0",
    "vc 14 0",
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  "summary": "Compute marginal effects at the mean or average marginal effects from  statistical models and returns the result as tidy data frames. These  data frames are ready to use with the 'ggplot2'-package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggaverage(), however, there are some convenient wrapper-functions especially for polynomials or interactions. There is a generic plot()-method to plot the results using 'ggplot2'."
}