Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) <doi:10.2307/1912771>, axiom-consistent subpopulations follow Crawford and Pendakur (2012) <doi:10.1111/j.1468-0297.2012.02545.x>.

R Markdown format for 'reveal.js' presentations, a framework 
  for easily creating beautiful presentations using HTML.

An implementation of the reverse ecology framework. Reverse ecology
    refers to the use of genomics to study ecology with no a priori assumptions
    about the organism(s) under consideration, linking organisms to their
    environment. It allows researchers to reconstruct the metabolic networks and
    study the ecology of poorly characterized microbial species from their
    genomic information, and has substantial potentials for microbial community
    ecological analysis.

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

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Tests linear regressions for significance reversal through leave-one(multiple)-out and shifting/addition of response values. The paradigm of the package is loosely based on the somewhat forgotten "dfstat" criterion (Belsley, Kuh & Welsch 1980 <doi:10.1002/0471725153.ch2>), which tests influential values in linear models from their effect on statistical inference, i.e. changes in p-value.