archetypal1.3.1 package

Finds the Archetypal Analysis of a Data Frame

plot_archs

A function for plotting arechetypes

print.archetypal

Print an object of the class archetypal.

study_AAconvergence

Function which studies the convergence of Archetypal Analysis when usi...

summary.archetypal

Summary for an object of the class archetypal.

align_archetypes_from_list

Align archetypes from a list either by the most frequent found or by u...

archetypal-package

Finds the Archetypal Analysis of a Data Frame

archetypal

archetypal: Finds the archetypal analysis of a data frame by using a v...

check_Bmatrix

Function which checks B matrix of Archetypal Analysis Y ~ A B Y in ord...

dirichlet_sample

Function which performs Dirichlet sampling

find_closer_points

Function which finds the data points that are closer to the archetypes...

find_furthestsum_points

Function which finds the furthest sum points in order to be used as in...

find_optimal_kappas

Function for finding the optimal number of archetypes

find_outmost_convexhull_points

Function which finds the outermost convex hull points in order to be u...

find_outmost_partitioned_convexhull_points

Function which finds the outermost convex hull points after making np ...

find_outmost_points

Function which finds the outermost points in order to be used as initi...

find_outmost_projected_convexhull_points

Function which finds the outermost projected convex hull points in ord...

find_pcha_optimal_parameters

Finds the optimal updating parameters to be used for the PCHA algorith...

FurthestSum

Application of FurthestSum algorithm in order to find an initial solut...

grouped_resample

Function for performing simple or Dirichlet resampling

kappa_tools

Compute kappa tools for data dimensionality analysis

plot.archetypal

Plot an object of the class archetypal.

plot.kappa_tools

Plot an object of the class kappa_tools

plot.study_AAconvergence

Plot an object of the class study_AAconvergence

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>.

  • Maintainer: Demetris Christopoulos
  • License: GPL (>= 2)
  • Last published: 2024-05-23