Get description of images from Clarifai API. For more information,
    see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.

Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) <doi:10.32614/RJ-2024-015>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.

Implementation of Clarke's distribution-free test of
  non-nested models.  Currently supported model functions are: 
  lm(), glm() ('binomial', 'poisson', 'negative binomial' links), 
  polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet').  
  For more information on the test, see Clarke (2007) 
  <doi:10.1093/pan/mpm004>.

Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.

Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.

Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.

Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.