Author: Sylvain Mangiarotti [aut], Flavie Le Jean [ctb], Malika Chassan [ctb],
Laurent Drapeau [ctb], Mireille Huc [cre]
Description: Platform dedicated to the Global Modelling technique. Its aim is to
obtain ordinary differential equations of polynomial form directly from
time series. It can be applied to single or multiple time series under various
conditions of noise, time series lengths, sampling, etc. This platform
is developped 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, projets GloMo, SpatioGloMo and MoMu).
The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged
(projects Crops'IChaos and Musc & SlowFast).
Suggests: testthat, knitr, rmarkdown, signal, float

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")
Description: Platform dedicated to the Global Modelling technique. Its aim
is to obtain ordinary differential equations of polynomial form directly
from time series. It can be applied to single or multiple time series under
various conditions of noise, time series lengths, sampling, etc. This platform
is developped 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, projets GloMo, SpatioGloMo and MoMu). The
French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects
Institut de Recherche pour le Développement [fnd],
Centre National de la Recherche Scientifique [fnd]

Title: Gaussian Process Ranking of Multiple Time Series
Authors@R: c(person("Hande", "Topa", role = c("aut", "cre"),
person("Antti", "Honkela", role = c("aut"),
Imports: gptk, matrixStats, tigreBrowserWriter, RColorBrewer
Description: 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.