in the first field. The result can be returned as a list or a data.table,
or even directly printed to an output file.

Authors@R: person("Jaroslav", "Kuchar", email = "jaroslav.kuchar@gmail.com", role = c("aut", "cre"))
Description: Algorithms for detection of outliers based on frequent pattern
mining. Such algorithms follow the paradigm: if an instance contains more frequent patterns,
it means that this data instance is unlikely to be an anomaly (He Zengyou, Xu Xiaofei, Huang
Zhexue Joshua, Deng Shengchun (2005) <doi:10.2298/CSIS0501103H>).
The package implements a list of existing state of the art algorithms as well as other published
approaches: FPI, WFPI, FPOF, FPCOF, LFPOF, MFPOF, WCFPOF and WFPOF.
Imports: pmml, XML, Matrix, R.utils, arules (>= 1.5-4), foreach,

Title: Segmentation using Optimal Partitioning and Function Pruning
Authors@R: c(person("Guillem", "Rigaill", email = "guillem.rigaill@inra.fr",
Description: A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles.
The 'fpop'  package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017
<doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated
in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error
for a penalty linear in the number of changepoints.

Title: Weighted Segmentation using Functional Pruning and Optimal
Authors@R: c(person("Guillem", "Rigaill", role = c("aut", "cre"),
Description: Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the 'capsushe' package.

Title: Computing the noncentrality parameter of the noncentral F
Description: Returns the noncentrality parameter of the noncentral F
ditribution if probability of type I and type II error, degrees
of freedom of the numerator and the denominator are given.  It
may be useful for computing minimal detecable differences for
general ANOVA models.  This program is documented in the paper
of A. Baharev, S. Kemeny, On the computation of the noncentral
F and noncentral beta distribution; Statistics and Computing,
paper is available at http://reliablecomputing.eu/ncbeta.html