Analyzes revisions in real-time time series vintages. The
    package converts between wide revision triangles and tidy long
    vintages, extracts selected releases, computes revision series,
    visualizes vintage paths, and summarizes revision properties such as
    bias, dispersion, autocorrelation, and news-noise diagnostics. It
    also identifies efficient releases and estimates state-space models
    for revision nowcasting. Methods are based on Howrey (1978)
    <doi:10.2307/1924972>, Jacobs and Van Norden (2011)
    <doi:10.1016/j.jeconom.2010.04.010>, and Kishor and Koenig (2012)
    <doi:10.1198/jbes.2010.08169>.

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 and enhances 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.