Package: archetypal 1.3.1

archetypal: Finds the Archetypal Analysis of a Data Frame

Performs archetypal analysis by using Principal Convex Hull Analysis under a full control of all algorithmic parameters. It contains a set of functions for determining the initial solution, the optimal algorithmic parameters and the optimal number of archetypes. Post run tools are also available for the assessment of the derived solution. Morup, M., Hansen, LK (2012) <doi:10.1016/j.neucom.2011.06.033>. Hochbaum, DS, Shmoys, DB (1985) <doi:10.1287/moor.10.2.180>. Eddy, WF (1977) <doi:10.1145/355759.355768>. Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) <doi:10.1145/235815.235821>. Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076>. Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), <doi:10.1093/qje/qjy013>. Christopoulos, DT (2015) <doi:10.1016/j.jastp.2015.03.009> . Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), <doi:10.3390/e21040394>.

Authors:Demetris Christopoulos [aut, cre], David Midgley [ctb], Sunil Venaik [ctb], INSEAD Fontainebleau France [fnd]

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archetypal.pdf |archetypal.html✨
archetypal/json (API)
NEWS

# Install 'archetypal' in R:
install.packages('archetypal', repos = c('https://dchristop.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • AbsoluteTemperature - Global Absolute Temperature data set for Northern Hemisphere 1969-2013
  • gallupGPS6 - Gallup Global Preferences Study processed data set of six variables
  • wd2 - 2D data set for demonstration purposes
  • wd25 - 2D data set created by 5 points for demonstration purposes
  • wd3 - 3D data set for demonstration purposes

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.18 score 1 packages 8 scripts 286 downloads 22 exports 17 dependencies

Last updated 6 months agofrom:5fe7dac558. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winOKNov 21 2024
R-4.5-linuxOKNov 21 2024
R-4.4-winOKNov 21 2024
R-4.4-macOKNov 21 2024
R-4.3-winOKNov 21 2024
R-4.3-macOKNov 21 2024

Exports:align_archetypes_from_listarchetypalcheck_Bmatrixdirichlet_samplefind_closer_pointsfind_furthestsum_pointsfind_optimal_kappasfind_outmost_convexhull_pointsfind_outmost_partitioned_convexhull_pointsfind_outmost_pointsfind_outmost_projected_convexhull_pointsfind_pcha_optimal_parametersFurthestSumgrouped_resamplekappa_toolsplot_archsplot.archetypalplot.kappa_toolsplot.study_AAconvergenceprint.archetypalstudy_AAconvergencesummary.archetypal

Dependencies:abindcodetoolsdoParallelentropyforeachgeometryinflectioniteratorslatticelinproglpSolvemagicMatrixmisc3dplot3DRcppRcppProgress

Introduction to Archetypal Package

Demetris T. Christopoulos

Rendered fromarchetypal.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2024-05-24
Started: 2019-06-13

Readme and manuals

Help Manual

Help pageTopics
Finds the Archetypal Analysis of a Data Framearchetypal-package
Global Absolute Temperature data set for Northern Hemisphere 1969-2013AbsoluteTemperature
Align archetypes from a list either by the most frequent found or by using a given archetypealign_archetypes_from_list
archetypal: Finds the archetypal analysis of a data frame by using a variant of the PCHA algorithmarchetypal
Function which checks B matrix of Archetypal Analysis Y ~ A B Y in order to find the used rows for creating each archetype and the relevant used weights.check_Bmatrix
Function which performs Dirichlet samplingdirichlet_sample
Function which finds the data points that are closer to the archetypes during all iterations of the algorithm PCHAfind_closer_points
Function which finds the furthest sum points in order to be used as initial solution in archetypal analysisfind_furthestsum_points
Function for finding the optimal number of archetypesfind_optimal_kappas
Function which finds the outermost convex hull points in order to be used as initial solution in archetypal analysisfind_outmost_convexhull_points
Function which finds the outermost convex hull points after making np samples and finding convex hull for each of them.find_outmost_partitioned_convexhull_points
Function which finds the outermost points in order to be used as initial solution in archetypal analysisfind_outmost_points
Function which finds the outermost projected convex hull points in order to be used as initial solution in archetypal analysisfind_outmost_projected_convexhull_points
Finds the optimal updating parameters to be used for the PCHA algorithmfind_pcha_optimal_parameters
Application of FurthestSum algorithm in order to find an initial solution for Archetypal AnalysisFurthestSum
Gallup Global Preferences Study processed data set of six variablesgallupGPS6
Function for performing simple or Dirichlet resamplinggrouped_resample
Compute kappa tools for data dimensionality analysiskappa_tools
A function for plotting arechetypesplot_archs
Plot an object of the class archetypal.plot.archetypal
Plot an object of the class kappa_toolsplot.kappa_tools
Plot an object of the class study_AAconvergenceplot.study_AAconvergence
Print an object of the class archetypal.print.archetypal
Function which studies the convergence of Archetypal Analysis when using the PCHA algorithmstudy_AAconvergence
Summary for an object of the class archetypal.summary.archetypal
2D data set for demonstration purposeswd2
2D data set created by 5 points for demonstration purposeswd25
3D data set for demonstration purposeswd3