The show_color() function is funny and easier to show colors.
The sym2poly() and sym2coef() function can extract coefficients from
Imports: ggplot2, clipr, Ryacas, magrittr, utils, grDevices, MASS,

Title: Automatic Colors for Multi-Dimensional Data
Description: Assign distinct colors to arbitrary multi-dimensional data, considering its structure.
person("Oren", "Ben-Kiki", email="oren@ben-kiki.org", role=c("aut", "cre")),
person("Weizmann Institute of Science", role="cph")
Imports: clue, ggplot2, grDevices, stats, umap

Title: Chandler-Bate Sandwich Loglikelihood Adjustment
person(c("Paul", "J."), "Northrop", email = "p.northrop@ucl.ac.uk",
person("Richard", "Chandler", role = c("aut", "cph"))
Description: Performs adjustments of a user-supplied independence loglikelihood
function using a robust sandwich estimator of the parameter covariance
matrix, based on the methodology in Chandler and Bate (2007)
<doi:10.1093/biomet/asm015>.  This can be used for cluster correlated data
when interest lies in the parameters of the marginal distributions or for
performing inferences that are robust to certain types of model
misspecification.  Functions for profiling the adjusted loglikelihoods are
also provided, as are functions for calculating and plotting confidence
intervals, for single model parameters, and confidence regions, for pairs
Imports: graphics, methods, numDeriv, stats

of model parameters.  Nested models can be compared using an adjusted
Imports: graphics, methods, numDeriv, stats, utils
Suggests: knitr, rmarkdown, sandwich, testthat

person(c("Richard", "E."), "Chandler", role = c("aut", "cph"))

Author: Matthias Wangler <mw@imbi.uni-freiburg.de>, Jan Beyersmann <jan@fdm.uni-freiburg.de>
Description: Change in length of hospital stay (LOS) is frequently used to assess the impact and the costs of hospital-acquired complications. In order to compute the attributable change in LOS, it is crucial to account for the timing of events: A complication can only have an effect on LOS, once it has occured. These temporal dynamics can be adequately handled by multistate models; however, there is few software for such models available.We introduce an R-package "changeLOS" for computing change in LOS based on methods described in Schulgen and Schumacher (1996). We will illustrate the program on data from a prospective cohort study on hospital-acquired infections. Main features of the R-package "changeLOS" are R-methods to: (1) describe the multi-state model. (2) compute the Aalen-Johansen estimator for the matrix of transition probabilities P(u-, u) for all observed transition times u. (3) compute the Aalen-Johansen estimator for the matrix of transition probabilities P(s,t); the estimator is a finite matrix product of matrices P(u-,u) for every observed event time in the interval(s,t]. (4) visualize the temporal dynamics of the data, illustrated by transition probabilities. (5) compute and visualize change in LOS. (6) compute bootstrap variances for change in LOS.