Title: Bayesian Ordinal Regression for High-Dimensional Data
Authors@R: c(person("Kellie J.", "Archer", email = "archer.43@osu.edu",
Description: 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
Imports: coda, devtools, dclone, runjags, DESeq2, SummarizedExperiment
Suggests: knitr, Biobase, testthat (>= 3.0.0), rmarkdown
Author: Kellie J. Archer [aut, cre] (<https://orcid.org/0000-0003-1555-5781>),

Depends: DESeq2, R (>= 2.10), SummarizedExperiment
Imports: coda, devtools, dclone, runjags

Title: Ordinal Regression for High-dimensional Data
Author: Kellie J. Archer, Jiayi Hou, Qing Zhou, Kyle Ferber, John G. Layne, Amanda Gentry
Description: 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.

Title: Ordinal Regression for High-Dimensional Data
Description: 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.

Description: 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.
Packaged: 2020-03-21 08:06:05 UTC; alexander

Packaged: 2022-10-06 14:36:21 UTC; alexander

Title: A 'tidyverse' Extension for Ordinations and Biplots
person("Jason Cory", "Brunson", email = "cornelioid@gmail.com",
person("Emily", "Paul", email = "erpb.71@gmail.com",
Description: Ordination comprises several multivariate exploratory and
explanatory techniques with theoretical foundations in geometric data
analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and
applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for
Greenacre (2010, ISBN:978-84-923846) shows how the most established of
these, including principal components analysis, correspondence analysis,
multidimensional scaling, factor analysis, and discriminant analysis,
rely on eigen-decompositions or singular value decompositions of
These decompositions give rise to a set of shared coordinates along which
the row and column elements can be measured. The overlay of their
scatterplots on these axes, introduced by Gabriel (1971)
<doi:10.1093/biomet/58.3.453>, is called a biplot.
'ordr' provides inspection, extraction, manipulation, and visualization
tools for several popular ordination classes supported by a set of recovery
methods. It is inspired by and designed to integrate into 'tidyverse'
workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
Imports: rlang, stringr, tidyselect, scales, generics, magrittr,
tibble, broom, tidyr, dplyr, purrr, labeling, ggrepel,
Suggests: testthat, sessioninfo, gridExtra, MASS, mlpack, vegan,
Collate: 'aaa-.r' 'biplot-key.r' 'biplot.r' 'data.r' 'dplyr-verbs.r'
Packaged: 2022-08-16 14:15:28 UTC; jason.brunson