Author: Adam Kapelner, David Azriel, Abba Krieger see COPYRIGHTS file for the authors of the java libraries
Description: These are GreedyExperimentalDesign Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without GreedyExperimentalDesign.
Packaged: 2018-07-15 08:29:01 UTC; AdamLenovo

Title: Greedy Stochastic Block Transition Models
Description: Performs clustering on the nodes of an undirected binary dynamic network, by maximising the exact integrated complete likelihood. The greedy algorithm used is described in Rastelli, R. (2017) "Exact integrated completed likelihood maximisation in a stochastic block transition model for dynamic networks" <arXiv:1710.03551>.
Packaged: 2017-10-18 08:29:58 UTC; rrastelli

Authors@R: c(person("Max", "Gordon", email = "max@gforge.se", role = c("aut", "cre")),
person("Reinhard", "Seifert", role = "aut", comment = "Author of original plotHR"))
Description: Methods for manipulating regression models and for describing these in a style adapted for medical journals.
Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model
estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots.
In addition to the descriptive methods, there are add-ons for the robust covariance matrix provided by the 'sandwich'
package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for
time-splitting a dataset when modeling non-proportional hazards in Cox regressions.
Imports: Hmisc, stringr, rms, sandwich, stats, nlme, methods,
htmlTable, magrittr, knitr, Epi, utils, graphics, grDevices
Suggests: boot, testthat, cmprsk, survival, dplyr, ggplot2, parallel
Reinhard Seifert [aut] (Author of original plotHR)

In addition to the descriptives methods, there are addons for the robust covariance matrix provided by the sandwich
package, a function for adding non-linearities to a model, and a wrapper around the Epi package's Lexis functions for
time-spliting a dataset when modeling non-proportional hazards in Cox regressions.
Suggests: boot, testthat, cmprsk, survival, dplyr, ggplot2