Co-clustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets
c( person("Serge", "Iovleff", role = c("aut","cre"), email = "Serge.Iovleff@stkpp.org")
, person("Parmeet", "Singh Bhatia", role = "aut", email = "bhatia.parmeet@gmail.com")
, person("Josselin", "Demont", role = "ctb")
, person("Vincent", "Brault", role = "ctb", email = "vincent.brault@imag.fr")
, person("Vincent", "Kubicki", role = "ctb", email = "vincent.kubicki@laposte.net")
, person("Gerard", "Goavert", role = "ctb")
, person("Christophe", "Biernacki", role = "ctb")
, person("Gilles", "Celeux", role = "ctb"))
Simultaneous clustering of rows and columns, usually designated by
biclustering, co-clustering or block clustering, is an important technique
in two way data analysis. It consists of estimating a mixture model which
takes into account the block clustering problem on both the individual and
variables sets. The 'blockcluster' package provides a bridge between the C++
core library build on top of the 'STK++' library, and the R statistical
computing environment. This package allows to co-cluster binary
<doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>,
continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets
<doi:10.1007/s11222-014-9472-2>. It also provides utility functions to
visualize the results. This package may be useful for various applications
in fields of Data mining, Information retrieval, Biology, computer vision
and many more. More information about the project and comprehensive tutorial
can be found on the link mentioned in URL.
Serge Iovleff [aut, cre],
Parmeet Singh Bhatia [aut],
Josselin Demont [ctb],
Vincent Brault [ctb],
Vincent Kubicki [ctb],
Gerard Goavert [ctb],
Christophe Biernacki [ctb],
Gilles Celeux [ctb]

Co-Clustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets

Change Point Detection for Multiple Aligned Independent Time
Series
c(person(given = "Lucas",
family = "Prates",
role = c("aut", "cre"),
email = "lucasdelprates@gmail.com",
comment = c(ORCID = "0000-0002-6431-8232")),
person(given = "Florencia",
family = "Leonardi",
role = c("aut"),
email = "florencia@usp.br",
comment = c(ORCID = "0000-0002-0299-0680"))
)
Implementation of statistical models based on
regularized likelihood for offline change point detection on multiple
aligned independent time series. It detects changes in
parameters for the specified family for the series as group or block.
As a reference for the method, see Prates et al. (2021) <arXiv:2111.10187>.
Lucas Prates [aut, cre] (<https://orcid.org/0000-0002-6431-8232>),
Florencia Leonardi [aut] (<https://orcid.org/0000-0002-0299-0680>)