A simplified version of the Portal Project Database designed for
    teaching. It provides a real world example of life-history, population, and
    ecological data, with sufficient complexity to teach many aspects of data
    analysis and management, but with many complexities removed to allow
    students to focus on the core ideas and skills being taught. The full
    database (which should be used for research) is available at
    <https://github.com/weecology/PortalData>.

Allows to limit the rate at which one or more functions can be called.

The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.

Fit statistical models based on the Dawid-Skene model - Dawid
    and Skene (1979) <doi:10.2307/2346806> - to repeated categorical
    rating data.  Full Bayesian inference for these models is supported
    through the Stan modelling language. 'rater' also allows the user to
    extract and plot key parameters of these models.

The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.