Performing Bayesian Inference for Repeated-Measures Designs
c(person(given = "Zhengxiao",
family = "Wei",
role = c("aut", "cre"),
email = "zhengxiao@uvic.ca",
comment = c(ORCID = "0000-0003-1866-2320")),
person(given = "Farouk S.",
family = "Nathoo",
role = c("aut"),
email = "nathoo@uvic.ca",
comment = c(ORCID = "0000-0002-2569-3507")),
person(given = "Michael E.J.",
family = "Masson",
role = c("aut"),
email = "mmasson@uvic.ca",
comment = c(ORCID = "0000-0002-5430-6078")))
A Bayesian credible interval is interpreted with respect to posterior probability,
and this interpretation is far more intuitive than that of a frequentist confidence interval.
However, standard highest-density intervals can be wide due to between-subjects variability and tends
to hide within-subjects effects, rendering its relationship with the Bayes factor less clear
in within-subjects (repeated-measures) designs.
This urgent issue can be addressed by using within-subjects intervals in within-subjects designs.

A Bayesian credible interval is interpreted with respect to posterior probability,
and this interpretation is far more intuitive than that of a frequentist confidence interval.
However, standard highest-density intervals can be wide due to between-subjects variability and tends
to hide within-subjects effects, rendering its relationship with the Bayes factor less clear
in within-subjects (repeated-measures) designs.
This urgent issue can be addressed by using within-subjects intervals in within-subjects designs,
which integrate four methods including the Loftus-Masson (1994) <doi:10.3758/BF03210951>,
the Rouder-Morey-Speckman-Province (2012) <doi:10.1016/j.jmp.2012.08.001>,
the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>,
and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.
methods, Rcpp (>= 0.12.0), RcppParallel, rstan (>= 2.18.1),
rstantools (>= 2.1.1), stats

RNA-Seq Analysis for Repeated-Measures Data
c(person("Yet", "Nguyen", email = "tienyettoan@gmail.com", role = c("aut", "cre")),
person("Dan", "Nettleton",  role = "aut"),
person(c("Charity", "Gordon"), c("Law", "Smyth"), role = "ctb",
comment = "The authors of limma::voom, which was modified slightly leading to my_voom"),
person("Agata", "Michna", role = "ctb",
comment = "The author of splineTimeR::splineDiffExprs, which was modified slightly leading to my_splineDiffExprs")
)
A differential expression analysis method for RNA-seq data from repeated-measures design
using general linear model framework and parametric bootstrap inference. The method accounts for
the dependence of gene expression levels due to the repeated-measures through continuous autoregressive
correlation structure. The method is described in Chapter 4 of
Nguyen (2018) <https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=7433&context=etd>.
AUC (>= 0.3.0), Biobase, DESeq2, edgeR (>= 3.16.5), graphics,
limma (>= 3.30.13), MASS (>= 7.3-45), Matrix (>= 1.2-8),
methods, nlme (>= 3.1-131), parallel, reshape (>= 0.8.6),
rlang, splines, splineTimeR, statmod (>= 1.4.29), stats
Yet Nguyen [aut, cre],
Dan Nettleton [aut],
Charity Gordon Law Smyth [ctb] (The authors of limma::voom, which was
modified slightly leading to my_voom),
Agata Michna [ctb] (The author of splineTimeR::splineDiffExprs, which
was modified slightly leading to my_splineDiffExprs)