Bayesian Ordinal Regression for High-Dimensional Data
c(person("Kellie J.", "Archer", email = "archer.43@osu.edu",
role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1555-5781")),
person("Anna", "Seffernick", role=c("ctb")),
person("Shuai", "Sun", role=c("ctb")),
person("Yiran", "Zhang", role=c("aut")))
Provides a function for fitting various penalized Bayesian
cumulative link ordinal response models when the number of parameters
exceeds the sample size. These models have been described in
Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.
coda, devtools, dclone, runjags, DESeq2, SummarizedExperiment
knitr, Biobase, testthat (>= 3.0.0), rmarkdown
Kellie J. Archer [aut, cre] (<https://orcid.org/0000-0003-1555-5781>),
Anna Seffernick [ctb],
Shuai Sun [ctb],
Yiran Zhang [aut]

DESeq2, R (>= 2.10), SummarizedExperiment

Ordinal Regression for High-dimensional Data
Kellie J. Archer, Jiayi Hou, Qing Zhou, Kyle Ferber, John G. Layne, Amanda Gentry
This package provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

Ordinal Regression for High-Dimensional Data
Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

c(
person("Alexander", "Duerre", role=c("aut","cre"), email="alexander.duerre@udo.edu"),
person("Alexander", "Schnurr", role=c("aut"), email="schnurr@mathematik.uni-siegen.de"))
Alexander Duerre [aut, cre],
Alexander Schnurr [aut]
Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) <doi:10.1007/s00362-013-0536-8> defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) <doi:10.1080/01621459.2016.1164706> are provided.