Imports: data.table, DT, htmltools, phosphoricons, readxl, rio, rlang,
shiny (>= 1.5.0), shinyWidgets (>= 0.5.3), tibble, tools
Suggests: jsonlite, knitr, MASS, rmarkdown, testthat, validate

Imports: data.table, htmltools, phosphoricons, reactable, readxl, rio,
rlang, shiny (>= 1.5.0), shinyWidgets (>= 0.5.3), tibble, tools

Imports: data.table, htmltools, htmlwidgets, phosphoricons, reactable,
readxl, rio, rlang, shiny (>= 1.5.0), shinyWidgets (>= 0.5.3),

readxl, rio, rlang, shiny (>= 1.5.0), shinyWidgets (>= 0.7.3),
Suggests: bslib, jsonlite, knitr, MASS, rmarkdown, testthat, validate

Title: Data and Functions to Accompany Analisis De Datos Con R
Authors@R: c(person("Christian", "Salas-Eljatib", role = c("aut", "cre","cph"),
email = "cseljatib@gmail.com", comment = c(ORCID = "0000-0002-8468-0829")),
person("Nicolas", "Pino", role = "aut"),
person("Joaquin", "Riquelme", role = "aut"))
Description: Datasets and Functions to Accompany Salas-Eljatib (2021, ISBN: 9789566086109)
"Analisis de datos con el programa estadistico R: una introduccion aplicada". The package helps carry out data management, exploratory analyses, and model fitting.
Packaged: 2022-11-02 21:26:22 UTC; christian
Author: Christian Salas-Eljatib [aut, cre, cph]

Packaged: 2022-11-05 12:51:22 UTC; christian

Package: datanugget
Type: Package
Title: Create, Refine, and Cluster Data Nuggets
Version: 1.0.0
Date: 2020-1-16
Authors@R: c(
person("Traymon", "Beavers", email = "tray.beavers@gmail.com", role = c("aut", "cre")),
person("Javier", "Cabrera", email = "xavier.cabrera@gmail.com", role = "aut"),
person("Mariusz", "Lubomirski", email = "mlubomir@its.jnj.com", role = "aut"))
Author: Traymon Beavers [aut, cre],
Javier Cabrera [aut],
Mariusz Lubomirski [aut]
Maintainer: Traymon Beavers <tray.beavers@gmail.com>
Description: Creating, refining, and clustering data nuggets.
Data nuggets reduce a large dataset into a small collection of nuggets of
data, each containing a center (location), weight (importance), and scale
(variability) parameter. Data nugget centers are created by choosing
observations in the dataset which are as equally spaced apart as possible.
Data nugget weights are created by counting the number observations
closest to a given data nugget’s center. We then say the data nugget
'contains' these observations and the data nugget center is recalculated
as the mean of these observations. Data nugget scales are created by
calculating the trace of the covariance matrix of the observations
contained within a data nugget divided by the dimension of the dataset.
Data nuggets are refined by 'splitting' data nuggets which have scales or
shapes (defined as the ratio of the two largest eigenvalues of the
covariance matrix of the observations contained within the data nugget)
deemed too large. Data nuggets are clustered by using a weighted form of
k-means clustering which uses both the centers and weights of data nuggets
to optimize the clustering assignments.
Depends: R (>= 3.5.0), doSNOW (>= 1.0.16), foreach (>= 1.4.4), parallel
(>= 3.5.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-01-16 20:25:52 UTC; tbeaver2
Repository: CRAN
Date/Publication: 2020-01-24 17:30:08 UTC