Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.

Use 'SQL' 'SELECT' statements to query 'R' data
    frames.

Tools to help to create tidy data, where each column is a
    variable, each row is an observation, and each cell contains a single
    value.  'tidyr' contains tools for changing the shape (pivoting) and
    hierarchy (nesting and 'unnesting') of a dataset, turning deeply
    nested lists into rectangular data frames ('rectangling'), and
    extracting values out of string columns. It also includes tools for
    working with missing values (both implicit and explicit).

Compute age-adjusted rates by direct and indirect methods and other epidemiological indicators in a tidy way, wrapping functions from the 'epitools' package.

Helper functions for processing 'REDCap' data in R. 'REDCap' is a 
    web-enabled application for building and managing surveys and databases 
    developed at Vanderbilt University.

Provides 'tidyverse' methods for wrangling
    and analyzing 'Earth Engine' <https://earthengine.google.com/> data. These methods help the user with filtering, 
    joining and summarising 'Earth Engine' image collections.

Allows users to quickly load multiple patients' electrocardiographic 
    (ECG) data at once and conduct relevant time analysis of heart rate variability 
    (HRV) without manual edits from a physician or data cleaning specialist. 
    The package provides the unique ability to iteratively filter, plot, 
    and store time analysis results in a data frame while writing plots to a 
    predefined folder. This streamlines the workflow for HRV analysis across 
    multiple datasets. Methods are based on Rodríguez-Liñares et al. (2011) 
    <doi:10.1016/j.cmpb.2010.05.012>. Examples of applications using this 
    package include Kwon et al. (2022) <doi:10.1007/s10286-022-00865-2> and 
    Lawrence et al. (2023) <doi:10.1016/j.autneu.2022.103056>.