Lyapunov Exponents and Kaplan-Yorke Dimension
c(
person("Sylvain", "Mangiarotti", email = "sylvain.mangiarotti@cesbio.cnes.fr",role = c("aut")),
person("Mireille", "Huc", email = "mireille.huc@cesbio.cnes.fr", role = c("cre", "aut")),
person("Institut de Recherche pour le Développement", role ="fnd"),
person("Centre National de la Recherche Scientifique", role ="fnd")
)
Estimation of the spectrum of Lyapunov Exponents and the Kaplan-Yorke
dimension of any low-dimensional model of polynomial form.
It can be applied, for example, to systems such as the chaotic
Lorenz-1963 system or the hyperchaotic Rossler-1979 system.
It can also be applied to dynamical models in Ordinary Differential Equations (ODEs) directly obtained
from observational time series using the 'GPoM' package.
The approach used is semi-formal,
the Jacobian matrix being estimated automatically from the polynomial equations.
Two methods are made available; one introduced by Wolf et al.
(1985) <doi:10.1016/0167-2789(85)90011-9>
and the other one introduced by Grond et al. (2003) <doi:10.1016/S0960-0779(02)00479-4>.
The package is provided with an interface for a more intuitive usage,
it can also be run without the interface.
This platform is developed at the Centre d'Etudes Spatiales de la Biosphere (CESBIO),
UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE.
The developments were funded by the French program Les Enveloppes Fluides
et l'Environnement (LEFE, MANU, projects GloMo, SpatioGloMo and MoMu). The
French programs Defi InFiNiTi (CNRS) and PNTS (CNRS) are also acknowledged
(projects Crops'I Chaos and Musc & SlowFast).
Sylvain Mangiarotti [aut],
Mireille Huc [cre, aut],
Institut de Recherche pour le Développement [fnd],
Centre National de la Recherche Scientifique [fnd]

Gaussian Process Ranking of Multiple Time Series
c(person("Hande", "Topa", role = c("aut", "cre"),
email = "hande.topa@helsinki.fi"),
person("Antti", "Honkela", role = c("aut"),
email = "antti.honkela@helsinki.fi"))
Implements a Gaussian process (GP)-based ranking method
which can be used to rank multiple time series according to their
temporal activity levels. An example is the case when expression
levels of all genes are measured over a time course and the main
concern is to identify the most active genes, i.e. genes which
show significant non-random variation in their expression levels.
This is achieved by computing Bayes factors for each time series
by comparing the marginal likelihoods under time-dependent and
time-independent GP models. Additional variance information from
pre-processing of the observations is incorporated into the GP
models, which makes the ranking more robust against model
overfitting. The package supports exporting the results to
'tigreBrowser' for visualisation, filtering or ranking.
Hande Topa [aut, cre],
Antti Honkela [aut]