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> or Sachs
and Gabriel (2022) <doi:10.18637/jss.v102.i09>. 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.

c(
person(given = "Alvaro", family = "Franquet", role = c("aut", "cre"), email = "afranquet@salutemporda.cat"),
person(given = "Maria Antonia", family = "Barceló", role = c("aut"), email = "antonia.barcelo@udg.edu", comment = c(ORCID = "0000-0001-9720-690X")),
person(given = "Marc", family = "Saez", role = c("aut"), email = "marc.saez@udg.edu", comment = c(ORCID = "0000-0003-1882-0157")),
person(given = "Pere", family = "Plaja", role = c("aut"), email = "pplaja@salutemporda.cat")
)
Event-driven programming is a programming paradigm where the flow of execution is defined by event.
In this paradigm an event can be defined as "a change in the state" of an object. This package offers a set
of functions for creating event-based architectures using three basic functions: events, dispatchers, and
handlers. The handlers manage the events, the dispatchers are in charge of redirecting the events to each of
the handlers, finally the events are the objects that carry the information about the change of state.
Alvaro Franquet [aut, cre],
Maria Antonia Barceló [aut] (<https://orcid.org/0000-0001-9720-690X>),
Marc Saez [aut] (<https://orcid.org/0000-0003-1882-0157>),
Pere Plaja [aut]

Stores, manipulates, aggregates and otherwise messes with event
data from KEDS/TABARI or any other extraction tool with similar output

Streaming Events and their Early Classification
Implements event extraction and early classification of events in data streams in R.
It has the functionality to generate 2-dimensional data streams with events belonging to
2 classes. These events can be extracted and features computed. The event features extracted
from incomplete-events can be classified using a partial-observations-classifier
(Kandanaarachchi et al. 2018) <doi:10.13140/RG.2.2.10051.25129>.
abind, tensorA, glmnet, AtmRay, dbscan, MASS, changepoint