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adamethods (version 1.2.1)

Archetypoid Algorithms and Anomaly Detection

Description

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) .

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Version

Install

install.packages('adamethods')

Monthly Downloads

261

Version

1.2.1

License

GPL (>= 2)

Maintainer

Guillermo Vinue

Last Published

August 4th, 2020

Functions in adamethods (1.2.1)

archetypoids_funct_multiv

Archetypoid algorithm with the functional multivariate Frobenius norm
archetypoids_norm_frob

Archetypoid algorithm with the Frobenius norm
adalara

Multivariate parallel archetypoid algorithm for large applications (ADALARA)
archetypoids_funct_multiv_robust

Archetypoid algorithm with the functional multivariate robust 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
archetypoids_funct

Archetypoid algorithm with the functional Frobenius norm
archetypoids_funct_robust

Archetypoid algorithm with the functional robust Frobenius norm
adalara_no_paral

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

Run the whole robust archetypoid analysis with the robust Frobenius norm
do_knno

kNN for outlier detection
do_fada_robust

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

Alphas and RSS of every set of archetypoids
frobenius_norm

Frobenius norm
frobenius_norm_funct

Functional Frobenius norm
frobenius_norm_funct_robust

Functional robust Frobenius norm
int_prod_mat

Interior product between matrices
int_prod_mat_funct

Interior product between matrices for FDA
frobenius_norm_robust

Robust Frobenius norm
do_clean_multiv

Cleaning multivariate functional outliers
fadalara_no_paral

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

Run the whole functional archetypoid analysis with the Frobenius norm
fadalara

Functional parallel archetypoid algorithm for large applications (FADALARA)
do_outl_degree

Degree of outlierness
do_clean

Cleaning outliers
do_alphas_rss_multiv

Alphas and RSS of every set of multivariate archetypoids
frobenius_norm_funct_multiv

Functional multivariate Frobenius norm
stepArchetypesRawData_norm_frob

Archetype algorithm to raw data with the Frobenius norm
frobenius_norm_funct_multiv_robust

Functional multivariate robust Frobenius norm
stepArchetypesRawData_robust

Archetype algorithm to raw data with the robust Frobenius norm
stepArchetypesRawData_funct_robust

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

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

Compute archetypes frame
outl_toler

Tolerance outliers
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

Archetype algorithm to raw data with the functional Frobenius norm
int_prod_mat_sq

Squared interior product between matrices
do_fada_multiv_robust

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

Squared interior product between matrices for FDA