S original by J. Lee, K. Hess and J. Dubin.
R port by Laura Pontiggia <pontla@stat.umn.edu>.
The function event.chart creates an event chart on the current
graphics device.

S original by J. Lee, K. Hess and J. Dubin. R port by Laura Pontiggia <pontla@stat.umn.edu>

Functions for analysis of rate changes in sequential events

Functions for analysis of rate changes in sequential events.

Event Prediction for Time-to-Event Endpoints
knitr, rmarkdown, rpact, fitdistrplus, gestate
Implements the hybrid framework for event prediction described in Fang & Zheng (2011, <doi:10.1016/j.cct.2011.05.013>). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential.

knitr, rmarkdown, rpact (>= 3.3.1), fitdistrplus, gestate

Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.

Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages. Janssenswillen et al. (2020) <http://ceur-ws.org/Vol-2703/paperTD7.pdf>.

Regression Models for Event History Outcomes
c(person(given = "Michael C",
family = "Sachs",
role = c("aut", "cre"),
email = "sachsmc@gmail.com"),
person(given = "Erin E",
family = "Gabriel",
role = "aut"),
person(given = "Morten", family = "Overgaard",
role = c("ctb"),
comment = "Corrected variance calculation"),
person(given = "Thomas A", family = "Gerds",
role = "ctb", comment = "Fast computation of leave one out cumulative incidence"),
person(given = "Terry", family = "Therneau",
role = "ctb",
comment = "Restricted mean computation"))
A user friendly, easy to understand way of doing event
history regression for marginal estimands of interest,
including the cumulative incidence and the restricted mean
survival, using the pseudo observation framework for
estimation. For a review of the methodology, see Andersen and
Pohar Perme (2010) <doi:10.1177/0962280209105020>. The
interface uses the well known formulation of a generalized
linear model and allows for features including plotting of
residuals, the use of sampling weights, and corrected
variance estimation.
testthat, prodlim, knitr, rmarkdown, rio, data.table
Michael C Sachs [aut, cre],
Erin E Gabriel [aut],
Morten Overgaard [ctb] (Corrected variance calculation),
Thomas A Gerds [ctb] (Fast computation of leave one out cumulative
incidence),
Terry Therneau [ctb] (Restricted mean computation)