An R-package for estimating absolute protein quantities from
label-free LC-MS/MS proteomics data
George Rosenberger, Hannes Roest, Christina Ludwig, Barry Grant
lattice, randomForest, ROCR, reshape2, testthat

aLFQ: George Rosenberger, Hannes Roest, Christina Ludwig. AAindex: Barry Grant.
lattice, randomForest, ROCR, reshape2, testthat, protiq

data.table, plyr, caret, seqinr, lattice, randomForest, ROCR,
reshape2, protiq

Estimating Absolute Protein Quantities from Label-Free LC-MS/MS
Proteomics Data
George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem
data.table, plyr, caret, seqinr, lattice, randomForest, ROCR,
reshape2, protiq, bio3d

data.table, plyr, caret, seqinr, lattice, randomForest, ROCR,
reshape2, bio3d

Automated Fitting of Moderated Nonlinear Factor Analysis Through
the 'Mplus' Program
c(
person("Veronica", "Cole", email = "vcole@email.unc.edu", role = c("aut","cre")),
person("Nisha", "Gottfredson", email = "gottfredson@email.unc.edu", role = c("aut")),
person("Michael", "Giordano", email = "mgiordano@email.unc.edu", role = c("aut")),
person("Tim", "Janssen", email = "tim_janssen@brown.edu", role = c("ctb")))
Automated generation, running, and interpretation of moderated
nonlinear factor analysis models for obtaining scores from observed
variables. This package creates 'Mplus' input files which may be run iteratively
to test two different types of covariate effects on items:
(1) latent variable impact (both mean and variance); and (2) differential
item functioning. After sequentially testing for all effects, it also creates a final
model by including all significant effects after adjusting for multiple comparisons.
Finally, the package creates a scoring model which uses the final values of parameter
estimates to generate latent variable scores.
grDevices, graphics, stats, utils, ggplot2, MplusAutomation,
reshape2, gridExtra, stringr, plyr, devtools
Veronica Cole [aut, cre],
Nisha Gottfredson [aut],
Michael Giordano [aut],
Tim Janssen [ctb]