Packaged: 2022-03-23 08:49:34 UTC; sfriedrich

Title: Rank Intraclass Correlation for Clustered Data
Description: Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous, ordinal, binary, and count data <https://github.com/shengxintu/rankICC>.
Packaged: 2022-09-20 16:10:05 UTC; shengxintu

Title: Methods for Working with Uncertainty in Rankings
Description: Provides methods for measuring and describing uncertainty in
rankings.  See Rising (2021) <arXiv:2107.03459> for background.

Description: Implements Kornbrot's rank difference test as described in
Wilcoxon signed-rank test which produces consistent and meaningful
results for ordinal or monotonically-transformed data.
Author: Brett Klamer [aut, cre] (<https://orcid.org/0000-0002-8334-4831>)

Description: Implements distance based probability models for ranking data.
The supported distance metrics include Kendall distance and Weighted Kendall distance.
Imports: Rcpp (>= 0.11.4), hash, optimx, methods

The supported models include Mallow's Phi-model (Kendall distance), Weighted Kendall distance model and Phi-Component model.
Imports: methods, Rcpp (>= 0.11.4), hash, optimx,

Imports: Rcpp (>= 0.11.4), hash, optimx, permute, methods

The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
and Weigthed-tau distance and geo-Weighted Kendall distance.

and Weighted-tau distance and geo-Weighted Kendall distance.

, Weighted-tau distance and Weighted Kendall distance.
Phi-component model and mixture models are also supported.

Weighted-tau distance and Weighted Kendall distance.

Package: rankhazard
Type: Package
Title: Rank-hazard plots
Version: 0.8-1
Date: 2012-03-17
Author: Juha Karvanen
Maintainer: Juha Karvanen <juha.karvanen@iki.fi>
Depends: survival
Suggests: rms
Description: 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
with respect to the reference hazard, which can be e.g. the
hazard related to the median of the covariate. Transformation
to scaled ranks allows plotting of covariates measured in
different units in the same graph, which helps in the
interpretation of the epidemiological relevance of the
covariates. Rank-hazard plots show the difference of hazards
between the extremes of the covariate values present in the
data and can be used as a tool to check if the proportional
hazards assumption leads to reasonable estimates for
individuals with extreme covariate values. Alternative
covariate definitions or different transformations applied to
covariates can be also compared using rank-hazard plots.
License: GPL-2
LazyLoad: yes
Packaged: 2012-03-18 15:38:27 UTC; Q
Repository: CRAN
Date/Publication: 2012-03-25 12:12:20