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lmms (version 1.3.3)

Linear Mixed Effect Model Splines for Modelling and Analysis of Time Course Data

Description

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.

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Version

Install

install.packages('lmms')

Monthly Downloads

65

Version

1.3.3

License

GPL (>= 2)

Maintainer

Last Published

March 7th, 2016

Functions in lmms (1.3.3)

investNoise

Quality control for time course profiles
filterNoise

Filter non-informative trajectories
lmmspline-class

lmmspline class a S4 class that extends lmms class.
lmmsDE

Differential expression analysis using linear mixed effect model splines.
plot.noise

Plot of associations objects
lmmsde-class

lmmsde class a S4 class that extends lmms class.
plot.lmmspline

Plot of lmmspline object
lmms-package

Data-driven mixed effect model splines fit and differential expression analysis
noise-class

noise S4 class
summary.lmmspline

Summary of a lmmspline Object
summary.noise

Summary of a noise Object
summary.lmmsde

Summary of a lmmsde Object
kidneySimTimeGroup

Kidney Simulation Data
predict.lmmspline

Predicts fitted values of an lmmspline Object
lmmSpline

Data-driven linear mixed effect model spline modelling
deriv.lmmspline

Derivative information for lmmspline objects
plot.lmmsde

Plot of lmmsde objects
lmms-class

lmms class a S4 superclass to extend lmmspline and lmmsde class.