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adamethods

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). Please see Vinue and Epifanio (2020) <doi:10.1007/s11634-020-00412-9>.

Functions (41)

adalara

Multivariate parallel archetypoid algorithm for large applications (ADALARA)

adalara_no_paral

Multivariate non-parallel archetypoid algorithm for large applications (ADALARA)

archetypoids_funct

Archetypoid algorithm with the functional Frobenius norm

archetypoids_funct_multiv

Archetypoid algorithm with the functional multivariate Frobenius norm

archetypoids_funct_multiv_robust

Archetypoid algorithm with the functional multivariate robust Frobenius norm

archetypoids_funct_robust

Archetypoid algorithm with the functional robust Frobenius norm

archetypoids_norm_frob

Archetypoid algorithm with the Frobenius norm

archetypoids_robust

Archetypoid algorithm with the robust Frobenius norm

bisquare_function

Bisquare function

do_ada

Run the whole classical archetypoid analysis with the Frobenius norm

do_ada_robust

Run the whole robust archetypoid analysis with the robust Frobenius norm

do_alphas_rss

Alphas and RSS of every set of archetypoids

do_alphas_rss_multiv

Alphas and RSS of every set of multivariate archetypoids

do_clean

Cleaning outliers

do_clean_multiv

Cleaning multivariate functional outliers

do_fada

Run the whole functional archetypoid analysis with the Frobenius norm

do_fada_multiv

Run the whole archetypoid analysis with the functional multivariate Frobenius norm

do_fada_multiv_robust

Run the whole archetypoid analysis with the functional multivariate robust Frobenius norm

do_fada_robust

Run the whole archetypoid analysis with the functional robust Frobenius norm

do_knno

kNN for outlier detection

do_outl_degree

Degree of outlierness

fadalara

Functional parallel archetypoid algorithm for large applications (FADALARA)

fadalara_no_paral

Functional non-parallel archetypoid algorithm for large applications (FADALARA)

frame_in_r

Compute archetypes frame

frobenius_norm

Frobenius norm

frobenius_norm_funct

Functional Frobenius norm

frobenius_norm_funct_multiv

Functional multivariate Frobenius norm

frobenius_norm_funct_multiv_robust

Functional multivariate robust Frobenius norm

frobenius_norm_funct_robust

Functional robust Frobenius norm

frobenius_norm_robust

Robust Frobenius norm

int_prod_mat

Interior product between matrices

int_prod_mat_funct

Interior product between matrices for FDA

int_prod_mat_sq

Squared interior product between matrices

int_prod_mat_sq_funct

Squared interior product between matrices for FDA

outl_toler

Tolerance outliers

stepArchetypesRawData_funct

Archetype algorithm to raw data with the functional Frobenius norm

stepArchetypesRawData_funct_multiv

Archetype algorithm to raw data with the functional multivariate Frobenius norm

stepArchetypesRawData_funct_multiv_robust

Archetype algorithm to raw data with the functional multivariate robust Frobenius norm

stepArchetypesRawData_funct_robust

Archetype algorithm to raw data with the functional robust Frobenius norm

stepArchetypesRawData_norm_frob

Archetype algorithm to raw data with the Frobenius norm

stepArchetypesRawData_robust

Archetype algorithm to raw data with the robust Frobenius norm

adamethods

Archetypoid Algorithms and Anomaly Detection

v1.2.1

GPL (>= 2)

Authors

Guillermo Vinue, Irene Epifanio

Initial release

2020-08-04

adamethods

Functions (41)

adamethods

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