Rank-hazard plots (Karvanen and Harrell, Statistics in Medicine 2009) visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.

Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.

Convenient Plotting for the Modified Rankin Scale and Other
Ordinal Outcome Data
Provides convenient tools for visualising ordinal outcome data following the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401>.

Statistical Tools for Preference Learning with Rankings and
Ratings
person("Michael", "Pearce", , "pearce790@gmail.com", role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0002-9313-271X"))
An implementation of the statistical methodology proposed by Pearce and Erosheva,
"A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review" (2022),
which at time of release has been accepted in the Journal of Machine Learning Research. The package provides tools
for estimating parameters of a Mallows-Binomial model, the first joint statistical preference
learning model for rankings and ratings. The package includes functions for simulating rankings and ratings from the model,
calculating the density of Mallows-Binomial data, estimating parameters using various exact and approximate algorithms,
and for obtaining approximate confidence intervals based on the nonparametric bootstrap.
Michael Pearce [aut, cre, cph]
(<https://orcid.org/0000-0002-9313-271X>)