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:10.1371/journal.pone.0236331>.
abind, tensorA, glmnet, dbscan, MASS, changepoint, dplyr

Ajay Shah, Vimal Balasubramaniam, Vikram Bahure
Implementation of short and long term event study
methodology

c(
person("Chirag", "Anand", role = c("aut", "cre"),
email = "anand.chirag@gmail.com"),
person("Vikram", "Bahure", role = "aut",
email = "economics.vikram@gmail.com"),
person("Vimal", "Balasubramaniam", role = "aut",
email = "vimsaa@gmail.com"),
person("Shekhar", "Harikumar", role = "ctb",
email = "shekhar.harikumar@gmail.com"),
person("Sargam", "Jain", role = "ctb",
email = "sargamjain13@gmail.com"),
person("Ajay", "Shah", role = "aut",
email = "ajayshah@mayin.org")
)
A platform for conducting event studies (Fama, Fisher,
Jensen, Roll (1969) <doi:10.2307/2525569>) and for
methodological research on event studies. The package
supports market model, augmented market model, and excess
returns methods for data modelling along with Wilcox,
classical t-test, and Bootstrap as inference procedures.
boot, graphics, methods, testthat, sandwich, stats, utils
Chirag Anand [aut, cre],
Vikram Bahure [aut],
Vimal Balasubramaniam [aut],
Shekhar Harikumar [ctb],
Sargam Jain [ctb],
Ajay Shah [aut]

An R package for conducting event studies and a platform
for methodological research on event studies.

Generalised Additive Extreme Value Models
Ben Youngman <b.youngman@exeter.ac.uk> [aut, cre]
Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.