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DESCRIPTION
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DESCRIPTION
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Package: baldur
Title: Bayesian Hierarchical Modeling for Label-Free Proteomics
Version: 0.0.3.9000
Authors@R:
person("Philip", "Berg", , "pb1015@msstate.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-3772-6185"))
Description: Statistical decision in proteomics data using a hierarchical
Bayesian model. There are two regression models for describing the
mean-variance trend, a gamma regression or a latent gamma mixture
regression. The regression model is then used as an Empirical Bayes
estimator for the prior on the variance in a peptide. Further, it assumes
that each measurement has an uncertainty (increased variance) associated
with it that is also inferred. Finally, it tries to estimate the posterior
distribution (by Hamiltonian Monte Carlo) for the differences in means for
each peptide in the data. Once the posterior is inferred, it integrates the
tails to estimate the probability of error from which a statistical decision
can be made.
See Berg and Popescu for details (<doi:10.1101/2023.05.11.540411>).
License: MIT + file LICENSE
URL: https://github.com/PhilipBerg/baldur
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Biarch: true
Depends:
R (>= 4.2.0)
Imports:
dplyr (>= 1.0.9),
magrittr (>= 2.0.3),
methods,
purrr (>= 0.3.4),
Rcpp (>= 0.12.0),
RcppParallel (>= 5.0.1),
rstan (>= 2.26.0),
rstantools (>= 2.2.0),
stats,
stringr (>= 1.0.4),
tidyr (>= 1.2.0),
rlang (>= 1.0.2),
Rdpack (>= 2.4),
multidplyr (>= 0.1.1),
ggplot2 (>= 3.3.6),
tibble (>= 3.1.7),
viridisLite (>= 0.4.1),
lifecycle
LinkingTo:
BH (>= 1.66.0),
Rcpp (>= 0.12.0),
RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1),
rstan (>= 2.26.0),
StanHeaders (>= 2.26.0)
SystemRequirements: GNU make
Suggests:
knitr,
rmarkdown
VignetteBuilder: knitr
LazyData: true
RdMacros: Rdpack
BugReports: https://github.com/PhilipBerg/baldur/issues