R/teda.R

In teda: An Implementation of the Typicality and Eccentricity Data Analysis Framework

#' teda: An implementation of the Typicality and Eccentricity Data Analysis
#'     Framework.
#'
#' @description
#'
#' The package provides functions to calculate both the batch and recursive
#'     typicality and eccentricity values of given observations.
#'
#' @details
#'
#' TEDA provides a non-parametric technique to determine how eccentric/typical
#' an observation is with respect to the other observations generated by the
#' same process.  Available as either a batch function working over a whole
#' dataset, or as a recursive one-time-pass function that needs the current
#' mean and variance values to be passed as arguments.
#'
#' Both batch and recursive methods return a datatype (tedab or tedar) which
#' provide print and summary generic function implementations.  The batch
#' object also provides a generic plot function.
#'
#' Further work will implement more of the analytical framework built up
#' around TEDA, such as clustering algorithms.
#'
#' @references
#'
#' Angelov, P., 2014. Outside the box: an alternative data analytics framework.
#' Journal of Automation Mobile Robotics and Intelligent Systems, 8(2), pp.29-35.
#' DOI: 10.14313/JAMRIS_2-2014/16
#'
#' Bezerra, C.G., Costa, B.S.J., Guedes, L.A. and Angelov, P.P., 2016, May.
#' A new evolving clustering algorithm for online data streams.
#' In Evolving and Adaptive Intelligent Systems (EAIS),
#' 2016 IEEE Conference on (pp. 162-168). IEEE.
#' DOI: 10.1109/EAIS.2016.7502508
#'
#' @docType package
#' @name teda
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