Linear Mixed Effect Model Splines for Modelling and Analysis of
Time Course Data
c(person("Jasmin", "Straube", role = c("aut", "cre"),email = "j.straube@qfab.org"),
person("Kim-Anh", "Le Cao", role = "aut"),
person("Emma", "Huang", role = "aut"),
person("Dominique", "Gorse", role = "ctb"))
Jasmin Straube [aut, cre],
Kim-Anh Le Cao [aut],
Emma Huang [aut],
Dominique Gorse [ctb]
stats, methods, nlme, lmeSplines, parallel, reshape2, gdata,
gplots, gridExtra
Linear Mixed effect Model Splines ('lmms') implements linear mixed
effect model splines for modelling and differential expression for highly
dimensional data sets: investNoise() for quality control and filterNoise() for
removing non-informative trajectories; lmmSpline() to model time course expression
profiles and lmmsDE() performs differential expression analysis to identify
differential expression between groups, time and/or group x time interaction.

Linear Mixed effect Model Splines (lmms) implements linear mixed effect model splines for
modelling and differential expression for highly dimensional data sets:
investNoise for quality control and filterNoise for removing
non-informative trajectories; lmmSpline to model time course expression
profiles and lmmsDE performs differential expression analysis to identify
differential expression between groups, time and/or group x time
interaction.

Computes model II simple linear regression using ordinary
least squares (OLS), major axis (MA), standard major axis (SMA), and
ranged major axis (RMA).

Computes model II simple linear regression using ordinary
least squares (OLS), major axis (MA), standard major axis
(SMA), and ranged major axis (RMA).

J. R. M. Hosking <hosking@watson.ibm.com>
Functions related to L-moments: computation of L-moments
of distributions and data samples; parameter estimation; L-moment
ratio diagram; plot vs. quantiles of an extreme-value distribution.

Functions related to L-moments: computation of L-moments
of distributions and data samples; parameter estimation;
L-moment ratio diagram; plot vs. quantiles of an extreme-value
distribution.