Processes and visualizes the output of complex phylogenetic analyses from the 'RevBayes' phylogenetic graphical modeling software.

A portable Shiny tool to explore patient-level electronic health record data 
    and perform chart review in a single integrated framework. This tool supports 
    browsing clinical data in many different formats including multiple versions 
    of the 'OMOP' common data model as well as the 'MIMIC-III' data model. In 
    addition, chart review information is captured and stored securely via the 
    Shiny interface in a 'REDCap' (Research Electronic Data Capture) project 
    using the 'REDCap' API. See the 'ReviewR' website for additional information, 
    documentation, and examples.

Extracts tagged text from markdown manuscripts for inclusion in dynamically generated revision letters. Provides an R markdown template based on papaja::revision_letter_pdf() with comment cross-referencing, a system for managing multiple sections of extracted text, and a way to automatically determine the page number of quoted sections from PDF manuscripts.

Tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.

We visualize the standard deviation of a data set as the radius of a cylinder whose volume equals the total volume of several cylinders made by revolving the empirical cumulative distribution function about the vertical line through the mean. For more details see Sarkar and Rashid (2016) <doi:10.1080/00031305.2016.1165734>.

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>.