# Robust statistical inference for quantitative LC-MS proteomics

## Documentation for package ‘msqrob2’ version 1.6.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

.StatModel | The StatModel class for msqrob
data | Example data for 100 proteins
getCoef | Accessor functions for StatModel class
getCoef-method | Accessor functions for StatModel class
getContrast | Methods for StatModel class
getContrast-method | Methods for StatModel class
getDF | Accessor functions for StatModel class
getDF-method | Accessor functions for StatModel class
getDfPosterior | Accessor functions for StatModel class
getDfPosterior-method | Accessor functions for StatModel class
getFitMethod | Accessor functions for StatModel class
getFitMethod-method | Accessor functions for StatModel class
getModel | Accessor functions for StatModel class
getModel-method | Accessor functions for StatModel class
getSigma | Accessor functions for StatModel class
getSigma-method | Accessor functions for StatModel class
getSigmaPosterior | Accessor functions for StatModel class
getSigmaPosterior-method | Accessor functions for StatModel class
getVar | Accessor functions for StatModel class
getVar-method | Accessor functions for StatModel class
getVarPosterior | Accessor functions for StatModel class
getVarPosterior-method | Accessor functions for StatModel class
getVcovUnscaled | Accessor functions for StatModel class
getVcovUnscaled-method | Accessor functions for StatModel class
hypothesisTest | Parameter estimates, standard errors and statistical inference on differential expression analysis
hypothesisTest-method | Parameter estimates, standard errors and statistical inference on differential expression analysis
hypothesisTestHurdle | Parameter estimates, standard errors and statistical inference on differential expression analysis
hypothesisTestHurdle-method | Parameter estimates, standard errors and statistical inference on differential expression analysis
makeContrast | Make contrast matrix
msqrob | Methods to fit msqrob models with ridge regression and/or random effects using lme4
msqrob-method | Methods to fit msqrob models with ridge regression and/or random effects using lme4
msqrobAggregate | Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein.
msqrobAggregate-method | Method to fit msqrob models with robust regression and/or ridge regression and/or random effects It models multiple features simultaneously, e.g. multiple peptides from the same protein.
msqrobGlm | Function to fit msqrob models to peptide counts using glm
msqrobHurdle | Function to fit msqrob hurdle models
msqrobHurdle-method | Function to fit msqrob hurdle models
msqrobLm | Function to fit msqrob models using lm and rlm
msqrobLmer | Function to fit msqrob models with ridge regression and/or random effects using lme4
msqrobQB | Function to fit msqrob models to peptide counts using glm
msqrobQB-method | Function to fit msqrob models to peptide counts using glm
pe | Example data for 100 proteins
show-method | The StatModel class for msqrob
smallestUniqueGroups | Smallest unique protein groups
StatModel | The StatModel class for msqrob
StatModel-class | The StatModel class for msqrob
StatModel-method | Methods for StatModel class
statModelAccessors | Accessor functions for StatModel class
statModelMethods | Methods for StatModel class
topFeatures | Toplist of DE proteins, peptides or features
varContrast | Methods for StatModel class
varContrast-method | Methods for StatModel class