Title: Utilise Catastrophe Model Event Loss Table Outputs
Description: Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.

Title: A package for computing reading time measures for
Author: Pavel Logacev <pavel.logacev@gmail.com>, Shravan Vasishth <vasishth@acm.org>
Description: em2 provides easy-to-use and fast functions for computing reading time measures used in psycholinguistic research based on eye tracking data.

Title: EM Algorithms for Estimating Item Response Theory Models
Author: Kosuke Imai <kimai@princeton.edu>, James Lo <lojames@usc.edu>, Jonathan Olmsted <jpolmsted@gmail.com>
Description: Various Expectation-Maximization (EM) algorithms are implemented for item response theory
(IRT) models. The current implementation includes IRT models for binary and ordinal
responses, along with dynamic and hierarchical IRT models with binary responses. The
latter two models are derived and implemented using variational EM.  Subsequent edits
also include variational network and text scaling models.

Author: Kosuke Imai <imai@harvard.edu>, James Lo <lojames@usc.edu>, Jonathan Olmsted <jpolmsted@gmail.com>
Description: Various Expectation-Maximization (EM) algorithms are implemented for item
response theory (IRT) models. The package includes IRT models for binary and ordinal
latter two models are fitted using variational EM.  The package also includes
variational network and text scaling models.  The algorithms are described in Imai, Lo,

Author: Kosuke Imai <kimai@princeton.edu>, James Lo <jameslo@princeton.edu>, Jonathan Olmsted <jpolmsted@gmail.com>
latter two models are derived and implemented using variational EM.

Title: General Tools for Building GLM Expectation-Maximization Models
Authors@R: person("Robert M.", "Cook", email="robert.cook@bcu.ac.uk", role=c("aut", "cre"), comment = c(ORCID = "0000-0003-3343-8271"))
Maintainer: Robert M. Cook <robert.cook@bcu.ac.uk>
Description: Implementation of Expectation Maximization (EM) regression of general linear models.
The package currently supports Poisson and Logistic regression with variable weights, with underlying theory included in the vignettes.
New users are recommended to look at the em.glm() and small.em() functions - the outputs of which are supported by AIC(), BIC(),  and logLik() calls.
Several plot functions have been included for useful diagnostics and model exploration.
Methods are based on the theory of Dempster et al (1977, ISBN:00359246), and follow the methods of Hastie et al. (2009) <doi:10.1007/978-0-387-21606-5_7>  and A.
Imports: MASS, pracma, stats, pander, pROC, AER
Author: Robert M. Cook [aut, cre] (<https://orcid.org/0000-0003-3343-8271>)