spelling, shiny, shinytest, stringr, rvest, xml2, ggplot2,
purrrlyr, testthat, markdown, knitr, rmarkdown, prettydoc,
Kendall, kableExtra, covr, readxl, glue, janitor, rclipboard

c(
person(c("James","E."), "Pustejovsky", email = "jepusto@gmail.com", role = c("aut", "cre")),
person("Man", "Chen", email = "manchen9005@gmail.com", role = "aut"),
person(c("Daniel","M."), "Swan", email = "dms031000@gmail.com", role = "aut"),
person("Paulina", "Grekov", email = "grekov.paulina@gmail.com", role = "ctb")
)
spelling, shiny, shinytest, stringr, rvest, xml2, ggplot2,
purrrlyr, testthat, markdown, knitr, rmarkdown, cleanrmd,
Kendall, kableExtra, covr, readxl, glue, janitor, rclipboard
James E. Pustejovsky [aut, cre],
Man Chen [aut],
Daniel M. Swan [aut],
Paulina Grekov [ctb]

Site Adaptation of Solar Irradiance Modeled Series
The SiteAdapt procedure improves the accuracy of modeled solar irradiance series
through site-adaptation with coincident ground-based measurements relying on the use of a
regression preprocessing followed by an empirical quantile mapping (eQM) correction.
Fernandez-Peruchena et al (2020) <doi:10.3390/rs12132127>.
glmulti, solaR, hyfo, hydroGOF, RColorBrewer, ggplot2, ggpubr,
stats

Inferring an Animal's Sites of Interest from High Resolution
Data
Within the area of movement ecology, it can be useful to identify an animal's sites of interest. These will be places that an animal uses a lot and so will spend a proportionately longer time there. The aim of this package is to identify both the position and size of these sites of interest from high resolution (sub-second) positional data.

Six Sigma Tools for Quality Control and Improvement
c(person("Emilio L.", "Cano", email = "emilio@lcano.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6101-9755")),
person("Javier M.", "Moguerza", role = c("aut")),
person("Mariano", "Prieto", role = c("aut")),
person("Andrés", "Redchuk", role = c("aut")),
person("Manuel", "Alfaro", role = c("ctb")))
Functions and utilities to perform
Statistical Analyses in the Six Sigma way.
Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
you can manage several Quality Management studies:
Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2]
and "Quality Control with R" [ISBN 978-3-319-24046-6],
are also included in the package.
grDevices, stats, graphics, lattice, ggplot2, reshape2,
nortest, e1071, scales, testthat, xtable
Emilio L. Cano [aut, cre] (<https://orcid.org/0000-0002-6101-9755>),
Javier M. Moguerza [aut],
Mariano Prieto [aut],
Andrés Redchuk [aut],
Manuel Alfaro [ctb]