person("Amanda", "Dobbyn", role = c("aut"),
Suggests: curl, jsonlite, knitr, multicolor, rmarkdown, roxygen2 (>=

Suggests: curl, jsonlite, knitr, multicolor, rmarkdown, testthat

Title: Duration-Based Quantities of Interest for the Cox Proportional
Depends: R (>= 2.13.0), rms, survival, mgcv
person("Kropko,", "Jonathan", email = "jkropko@virginia.edu", role = c("aut", "cre")),
person("Harden,", "Jeffrey J.", email = "jeff.harden@nd.edu", role = c("aut")))
Description: Functions for generating, simulating, and visualizing expected
durations and marginal changes in duration from the Cox proportional hazards
Imports: PermAlgo, dplyr, tidyr, ggplot2, gridExtra, utils

Depends: R (>= 3.5.0), rms, survival, mgcv
Description: Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.

Imports: PermAlgo, dplyr, tidyr, ggplot2, gridExtra, mediation, utils

Title: Cox-type models for interval-censored data
Depends: Matrix, parallel, survival, timereg, R (>= 2.13.0)
Author: Audrey Boruvka and Richard J. Cook
Description: Fits Cox-type models based on interval-censored data from a survival

Title: Cox-Type Models for Interval-Censored Data
Depends: R (>= 2.15.2), survival, timereg

Depends: survival, bdsmatrix, R (>= 2.0.0)
Description: Cox proportional hazards models containing Gaussian random

Depends: survival, bdsmatrix, nlme, R (>= 2.12.0)

Depends: survival, bdsmatrix (>= 1.3), nlme, Matrix (>= 1.0), methods,

Depends: survival (>= 2.36.14), methods, bdsmatrix(>= 1.3)
Authors@R: person(c("Terry", "M."), "Therneau", role=c("aut", "cre"),
Maintainer: Terry M. Therneau <therneau@mayo.edu>

Depends: survival (>= 2.36.14), methods, bdsmatrix(>= 1.3), R(>= 2.10)
Description: This package fits Cox proportional hazards models containing both
fixed and random effects.  The random effects can have a general form, of which
familial interactions (a "kinship" matrix) is a particular special case.
Note that the simplest case of a mixed effects Cox model, i.e. a single random
per-group intercept, is also called a "frailty" model.  The approach is based
on Rippatti and Palgren, Biometrics 2002.