When building complex models, it is often difficult to explain why
    the model should be trusted. While global measures such as accuracy are
    useful, they cannot be used for explaining why a model made a specific
    prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
    explaining the outcome of black box models by fitting a local model around
    the point in question an perturbations of this point. The approach is
    described in more detail in the article by Ribeiro et al. (2016) 
    <arXiv:1602.04938>.

Provides an interface to the financial data platform <https://datahub.limex.com/>., enabling users to retrieve real-time and historical financial data. Functions within the package allow access to instruments, candlestick charts, fundamentals, news, events, models, and trading signals. Authentication is managed through user-specific API tokens, which are securely handled via environment variables.

Compose interactive visualisations designed for exploratory 
        high-dimensional data analysis. With 'liminal' you can create linked
        interactive graphics to diagnose the quality of a dimension reduction
        technique and explore the global structure of a dataset with a tour. A
        complete description of the method is discussed in 
        ['Lee' & 'Laa' & 'Cook' (2020) <arXiv:2012.06077>].

Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).

'LimeSurvey' is Free/Libre Open Source Software for
  the development and administrations of online studies, using
  sophisticated tailoring capabilities to support multiple study
  designs (see <https://www.limesurvey.org>). This package supports
  programmatic creation of surveys that can then be imported into
  'LimeSurvey', as well as user friendly import of responses from
  'LimeSurvey' studies.

A flexible approach, inspired by cosinor regression, for
  differential analysis of rhythmic transcriptome data. See Singer and Hughey
  (2018) <doi:10.1177/0748730418813785>.