Partially Linear Additive Quantile Regression
Estimation, prediction, thresholding, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
person("Adam", "Maidman",  role = c("cre","aut"), email =  "maidm004@umn.edu")

Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.

Read, Tidy, and Display Data from Microtiter Plates
person("Sean", "Hughes", email = "smhughes@uw.edu",
role = c("aut", "cre"))
Tools for interacting with data from experiments done in microtiter
plates. Easily read in plate-shaped data and convert it to tidy format,
combine plate-shaped data with tidy data, and view tidy data in plate shape.

person("Sean", "Hughes", email = "smhughes@uw.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9409-9405"))

person("Scott", "Warchal", email = "s.warchal@sms.ed.ac.uk", role = c("aut", "cre"))
Collection of functions for working with multi-well microtitre
plates, mainly 96, 384 and 1536 well plates.
dplyr, ggplot2, plyr, lazyeval, raster, RColorBrewer, MASS,
viridis

person("Scott", "Warchal", email = "scott.warchal@gmail.com", role = c("aut", "cre"))

Data from a Test of the PlatoWork tDCS Headset
person(given = "Lasse",
family = "Hjorth Madsen",
role = c("aut", "cre"),
email = "lassehjorthmadsen@gmail.com")
Data and analysis from an experiment with improving touch
typing speed, using the tDCS PlatoWork headset produced by PlatoScience.
dplyr, ggplot2, knitr, purrr, rmarkdown, testthat

c(
person( "Michail", "Tsagris", role = c("aut", "cre"), email = "mtsagris@uoc.gr" ),
person("Christos", "Adam", role = "aut", email = "pada4m4@gmail.com") )
Michail Tsagris [aut, cre],
Christos Adam [aut]
It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.