Authors@R: c(person(given="David", family="DEVREKER", role = c("aut"), email =
of temporal trend analysis in the framework of the OSPAR convention.
It is based on four successive steps : simply manipulate your database,
select the parameters you want to analyse, build your regulated
time series, perform analysis and read the results all in one easy to use
interface. Statistical analysis call other package function such as
Depends: R (>= 3.0.0), base, stats, grDevices
Imports: Hmisc, pastecs, reshape, wq, e1071 , timeSeries, fBasics ,
relimp, multcomp, nlme, tcltk, lubridate, tcltk2

to analyse temporal trend in the framework of the OSPAR convention.
The analysis process run through 4 successive steps : 1) manipulate your data, 2)
select the parameters you want to analyse, 3) build your regulated
time series, 4) perform diagnosis and analysis and 5) read the results.
Statistical analysis call other package function such as Kendall tests
Imports: pastecs, reshape, wq, e1071, relimp, multcomp , nlme,

Authors@R: c(person(given="David", family="DEVREKER", role = c("aut", "cre"), email =
role = c("aut"), email = "Alain.Lefebvre@ifremer.fr" ) )
Imports: pastecs, reshape, e1071, relimp, multcomp , nlme, lubridate,

Depends: R (>= 3.3.0), base, stats, grDevices
Imports: pastecs, reshape, e1071, relimp, multcomp, rkt , nlme,
lubridate, tcltk, tcltk2, mvtnorm, zoo, methods

Imports: pastecs, reshape, e1071, relimp, multcomp, rkt, stlplus, nlme,

It is based on 4 successive steps : simply manipulate your database,

Description: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response
following stimulation. A common observation in this type of data is that some genes respond with quick,
transient dynamics, while other genes change their expression slowly over time. The existing methods for the
detection of significant expression dynamics often fail when the expression dynamics show a large
heterogeneity, and often cannot cope with irregular and sparse measurements. The method proposed here is
specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and
transient dynamics as well as slow expression changes, and deals with low replicate numbers and irregular
sampling times. The results are given in the form of tables linked to figures. These allow to quickly recognize
the relevance of detection, to identify possible false positives and to distinguish between changes in the early
and later expression. An extension of the method allows the analysis of the expression dynamics of functional
groups of genes, providing a quick overview of the cellular response. The performance of this package was
tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor.
See publication "TTCA: An R package for the identification of differentially expressed genes in time course microarray data".
Imports: Matrix, quantreg, tcltk2, RISmed, VennDiagram, MASS