Implements the estimation techniques described in Rousseeuw &
    Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the location and
    scale of very small samples.

Interaction with "RevBayes" via R. Objects created in "RevBayes" can be passed into the R environment, and many types can be converted into similar R objects. To download "RevBayes", go to <https://revbayes.github.io/download>.

Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.

Facilitates making a connection to the 
  'Revulytics' API and executing various queries. You can use it to
  get event data and metadata. The Revulytics documentation 
  is available at <https://docs.revenera.com/ui560/report/>. This
  package is not supported by 'Flexera' (owner of the software).

Diagnostics and data preparation for random effects within estimator, random effects 
    within-idiosyncratic estimator, between-within-idiosyncratic model, and cross-classified between model.
    Mundlak, Yair (1978) <doi:10.2307/1913646>.
    Hausman, Jeffrey (1978) <doi:10.2307/1913827>.
    Allison, Paul (2009) <doi:10.4135/9781412993869>.
    Neuhaus, J.M., and J. D. Kalbfleisch (1998) <doi:10.2307/3109770>.