Identify Distributions that Match Reported Sample Parameters
(SPRITE)
person(given = "Lukas",
family = "Wallrich",
role = c("aut", "cre"),
email = "lukas.wallrich@gmail.com",
comment = c(ORCID = "0000-0003-2121-5177"))
The SPRITE algorithm creates possible distributions of discrete responses
based on reported sample parameters, such as mean, standard deviation and range
(Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it,
drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <http://shiny.ieis.tue.nl/sprite/>.
In addition, it supports the modeling of distributions based on multi-item (Likert-type)
scales and the use of restrictions on the frequency of particular responses.
ggplot2, testthat (>= 3.0.0), tibble, tidyr, scales
Lukas Wallrich [aut, cre] (<https://orcid.org/0000-0003-2121-5177>)

c(person(given = "Lukas",
family = "Wallrich",
role = c("aut", "cre"),
email = "lukas.wallrich@gmail.com",
comment = c(ORCID = "0000-0003-2121-5177")),
person(given = "Aurélien",
family = "Allard",
role = c("ctb"),
email = "aurelien.ab.allard@gmail.com"))
The SPRITE algorithm creates possible distributions of discrete responses
based on reported sample parameters, such as mean, standard deviation and range
(Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it,
drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <https://shiny.ieis.tue.nl/sprite/>.
In addition, it supports the modeling of distributions based on multi-item (Likert-type)
scales and the use of restrictions on the frequency of particular responses.
Lukas Wallrich [aut, cre] (<https://orcid.org/0000-0003-2121-5177>),
Aurélien Allard [ctb]

R interface to SPRNG (Scalable Parallel Random Number Generators)
Provides interface to SPRNG APIs, and examples and
documentation for its use.

Provides interface to SPRNG 2.0 APIs, and examples and
documentation for its use.

R interface to SPRNG (Scalable Parallel Random Number
Generators)

Calculates a newly defined coefficient of determination, aka R^2, and coefficient of partial determination, aka partial R^2 by Zhang (2016) for generalized linear models (including quasi models with well defined variance functions). It also avoids overstatement of variation proportion explained by the model or a group of covariates.