Package: bigmds
Title: Multidimensional Scaling for Big Data
Version: 0.0.1
Authors@R: 
    c(
    person(given = "Cristian",
           family = "Pachón García",
           role = c("aut", "cre"),
           email = "cc.pachon@gmail.com",
           comment = c(ORCID = "0000-0001-9518-4874")),
    person(given = "Pedro",
           family = "Delicado",
           role = c("aut"),
           email = "pedro.delicado@upc.edu",
           comment = c(ORCID = "0000-0003-3933-4852"))
    )
Description: We present a set of algorithms for Multidimensional Scaling (MDS) to be used with large datasets. 
  MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. 
  When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained.
  With this package, we address these problems by means of three algorithms: Divide and Conquer MDS, Fast MDS and 
  MDS based on Gower interpolation. The main idea of these methods is based on partitioning the dataset into small 
  pieces, where classical methods can work.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: MCMCpack, stats, pdist
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
URL: https://github.com/pachoning/bigmds
BugReports: https://github.com/pachoning/bigmds/issues
NeedsCompilation: no
Packaged: 2021-01-14 17:39:05 UTC; cristianpachongarcia
Author: Cristian Pachón García [aut, cre]
    (<https://orcid.org/0000-0001-9518-4874>),
  Pedro Delicado [aut] (<https://orcid.org/0000-0003-3933-4852>)
Maintainer: Cristian Pachón García <cc.pachon@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-18 16:20:10 UTC
