testthat, covr, rmarkdown, vdiffr, openintro, patchwork,
viridis, readr

testthat, covr, rmarkdown, vdiffr, openintro, patchwork,
viridis, readr, nycflights13

Albert Y. Kim [aut, cre] (<https://orcid.org/0000-0001-7824-306X>),
Chester Ismay [aut] (<https://orcid.org/0000-0003-2820-2547>),
Andrew Bray [ctb] (<https://orcid.org/0000-0002-4037-7414>),
Delaney Moran [ctb],
Evgeni Chasnovski [ctb] (<https://orcid.org/0000-0002-1617-4019>),
Will Hopper [ctb] (<https://orcid.org/0000-0002-7848-1946>),
Benjamin S. Baumer [ctb] (<https://orcid.org/0000-0002-3279-0516>),
Marium Tapal [ctb] (<https://orcid.org/0000-0001-5093-6462>),
Wayne Ndlovu [ctb],
Catherine Peppers [ctb],
Annah Mutaya [ctb],
Anushree Goswami [ctb],
Ziyue Yang [ctb] (<https://orcid.org/0000-0002-9299-8327>),
Clara Li [ctb] (<https://orcid.org/0000-0003-2456-0849>),
Caroline McKenna [ctb],
Catherine Park [ctb] (<https://orcid.org/0000-0002-8273-9620>),
Abbie Benfield [ctb],
Georgia Gans [ctb],
Kacey Jean-Jacques [ctb],
Swaha Bhattacharya [ctb],
Vivian Almaraz [ctb],
Elle Jo Whalen [ctb],
Jacqueline Chen [ctb],
Michelle Flesaker [ctb],
Irene Foster [ctb],
Aushanae Haller [ctb],
Benjamin Bruncati [ctb] (<https://orcid.org/0000-0001-8545-5984>),
Quinn White [ctb] (<https://orcid.org/0000-0001-5399-0237>),
Tianshu Zhang [ctb] (<https://orcid.org/0000-0002-3004-4472>),
Katelyn Diaz [ctb] (<https://orcid.org/0000-0001-6108-1682>),
Rose Porta [ctb],
Renee Wu [ctb],
Arris Moise [ctb],
Kate Phan [ctb],
Grace Hartley [ctb],
Silas Weden [ctb],
Emma Vejcik [ctb] (<https://orcid.org/0000-0001-5093-6462>),
Nikki Schuldt [ctb],
Tess Goldmann [ctb],
Hongtong Lin [ctb],
Alejandra Munoz [ctb],
Elina Gordon-Halpern [ctb],
Haley Schmidt [ctb] (<https://orcid.org/0000-0002-6184-2266>)

Find the Modes and Assess the Modality of Complex and Mixture
Distributions, Especially with Big Datasets
Sathish Deevi [aut, cre],
4D Strategies [aut,own]
Designed with a dual purpose of
accurately estimating the mode (or modes) as well as characterizing
the modality of data. The specific application area includes complex
or mixture distributions particularly in a big data environment.
The heterogeneous nature of (big) data may require deep introspective
statistical and machine learning techniques, but these statistical tools
often fail when applied without first understanding the data. In small
datasets, this often isn't a big issue, but when dealing with large scale
data analysis or big data thoroughly inspecting each dimension
typically yields an O(n^n-1) problem. As such, dealing with big data
require an alternative toolkit. This package not only identifies the
mode or modes for various data types, it also provides a programmatic
way of understanding the modality (i.e. unimodal, bimodal, etc.) of
a dataset (whether it's big data or not). See
<http://www.sdeevi.com/modes_package> for examples and discussion.