Archetypoid Algorithms and Anomaly Detection

Collection of several algorithms to obtain archetypoids with small and large databases and with both classical multivariate data and functional data (univariate and multivariate). Some of these algorithms also allow to detect anomalies (outliers).


Package: adamethods

Version: 1.2 [2019-03-06]

  • suppressWarnings(RNGversion("3.5.0")) has been inserted before calling set.seed() in the examples, following CRAN maintainers' recommendations.
  • The 'Value' and 'Examples' section of do_knno() have been updated.
  • A typo in the ADALARA and FADALARA algorithms has been corrected about the update of the set of the random observations sampled after the computation of the frame (rand_obs_si <- rand_obs_si[si_frame]).
  • References have been updated.
  • Submitted to CRAN.

Version: 1.1 [2018-11-26]

  • An R function to use the frame approach inside the ADALARA and FADALARA algorithms have been included. This function was kindly provided by Sebastian Mair (see Mair S., Boubekki A. and Brefeld U. (2017) 'Frame-based data factorizations'. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 1-9,
  • References have been accurately corrected.
  • Submitted to CRAN.

Version: 1.0 [2018-11-05]

  • First package version.
  • Submitted to CRAN.

Reference manual

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1.2 by Guillermo Vinue, a year ago

Browse source code at

Authors: Guillermo Vinue , Irene Epifanio

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Anthropometry, archetypes, FNN, foreach, graphics, nnls, parallel, stats, tolerance, univOutl, utils

Suggests doParallel, fda

See at CRAN