Provides a 'teal_data' class as a unified data model for
    'teal' applications focusing on reproducibility and relational data.

Utilizing the 'logger' framework to record events within a
    package, specific to 'teal' family of packages.  Supports logging
    namespaces, hierarchical logging, various log destinations,
    vectorization, and more.

Provides user-friendly tools for creating and customizing
    clinical trial reports. By leveraging the 'teal' framework, this
    package provides 'teal' modules to easily create an interactive panel
    that allows for seamless adjustments to data presentation, thereby
    streamlining the creation of detailed and accurate reports.

Prebuilt 'shiny' modules containing tools for viewing data,
    visualizing data, understanding missing and outlier values within your
    data and performing simple data analysis.  This extends 'teal'
    framework that supports reproducible research and analysis.

Prebuilt 'shiny' modules containing tools for the generation
    of 'rmarkdown' reports, supporting reproducible research and analysis.

Data filtering module for 'teal' applications.  Allows for
    interactive filtering of data stored in 'data.frame' and
    'MultiAssayExperiment' objects. Also displays filtered and unfiltered
    observation counts.

A standardized user interface for column selection, that
    facilitates dataset merging in 'teal' framework.

Collection of 'shiny' widgets to support 'teal' applications.
    Enables the manipulation of application layout and plot or table
    settings.

Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately  as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.